Industry solution
One trusted number, checked on its way in and answered with its source
Metric definitions, data freshness, access and residency, cited answers and the data team's roadmap, joined up for data and BI teams.
The problem
Every team has its own version of the key figures, loads fail without anyone noticing, access to customer data is decided in email threads, and the data team spends its week answering questions instead of planning the work that would stop them.
Most data teams no longer lack tools; they lack agreement. A figure such as net sales, active customers or retention is rebuilt in every dashboard, spreadsheet and notebook that needs it, each with slightly different rules for returns, discounts, time zones or cancellations. When the figures disagree, leaders stop trusting any of them, and analysts spend their weeks reconciling numbers instead of explaining them.
The plumbing underneath fails in quieter ways. A source system changes its export layout without warning, an overnight load finishes with half the rows, and a dashboard keeps its usual shape with no hint that anything is missing. Meanwhile requests for customer data pile up, residency rules keep some records inside a region, and the cloud warehouse bill grows with every board that refreshes more often than anyone needs.
The team itself is stretched. Requests arrive through chat, email and corridor conversations, the same question is asked five different ways, and the roadmap competes with platform work that only one or two people know how to do. Skills move faster than hiring, so the gap between what the business asks of its data team and what the team can deliver keeps widening. Each tool does its own job; what breaks is the handover between them.
Head of data or chief data officer
One set of metrics the leadership team stops arguing about, a roadmap the business can see, and data spend that can be explained line by line.
Analytics engineering lead
Metric definitions in one place with a change history, changes reviewed with their impact first, and no more rebuilding the same logic in every dashboard.
BI and analytics manager
Dashboards people trust, routine questions answered with their source, and analysts freed from the ask-data queue for real analysis.
Data platform or data engineering lead
Loads that fail loudly, freshness visible for every table that feeds a board, source health tracked, and warehouse cost tied to what drives it.
Data protection or governance lead
Access to customer data decided by purpose with an end date, personal records kept in their home region, and one record of who saw what.
Decision-intelligence or strategy analytics lead
Recommendations built on agreed definitions and a known data date, so they survive the first skeptical question from leadership.
A day in the life
Tomás leads a data team of fourteen: data engineers, analytics engineers, analysts and one data product manager. They serve finance, merchandising, stores, e-commerce and marketing on both sides of the Atlantic. The team is capable and the tools are modern. What wears them down is the space between the tools: figures that disagree, loads that fail quietly, access decided in email and a request list nobody can see. This is one ordinary week in late September.
Monday, 09:10
The weekly trading meeting opens with three numbers for last week's net sales: $4.61 million from finance, $5.02 million on the e-commerce flash and $4.77 million on the stores board. E-commerce counts orders at checkout with shipping fees, finance books sales at shipment after returns and discounts, and the stores subtract every return taken at a till. Priyanka Rao, the senior analytics engineer, drafted a finance-aligned rule weeks ago; it is still unpublished because nobody can say which boards, loads and chat answers it would move, or by how much. Helena Marsh, the CFO, asks Tomás for one number everyone uses by the end of the month.
How this is solved: A net sales rule change nobody could sizeTuesday, 07:20
The daily sales flash shows Monday store sales down by nearly a third. Before Tomás has finished his coffee, the regional vice president has emailed two store managers asking what went wrong. Nothing did. The till system's overnight export changed its layout, the load brought in no rows for 23 of the 70 stores, and the job still reported success. Luca Bianchi, the data engineer on call, spends the morning proving that the stores had a normal Monday.
How this is solved: A sales dip that was really a missing fileWednesday, 16:40
Jordan Ellis, a marketing analyst in Denver, asks for the European loyalty member list to build a win-back audience of people who have not bought in a year, and he wants it by Friday. Company policy keeps EU customer records in the EU region and limits their use to what members agreed to. The last similar request was approved in an email thread, and a spreadsheet of names and email addresses surfaced in a shared drive months later. Sabine Krüger, the data protection lead in Amsterdam, wants to see exactly what would be shared, and why.
How this is solved: An access request that crosses a borderThursday, 10:15
The ask-data chat channel fills up: returns at the Denver stores, whether tents are ahead of last year, why web conversion dipped on Tuesday. Most of the answers already sit on a dashboard the asker could not find or did not trust. Mei-Lin Chau, the analytics lead, and one of her analysts answer them one by one while the pricing analysis for the leadership team waits, and a regional sales manager pastes a figure from a public chatbot that matches nothing in any system.
How this is solved: Forty quick questions before lunchThursday, 15:30
Finance forwards the August invoice for the cloud data warehouse, well above July, with one line: what happened? Luca suspects the new marketing attribution model. Mei-Lin suspects the store operations wall board, which refreshes every five minutes around the clock. The warehouse's own console shows spend by compute cluster, not by team or dashboard, and the contract renewal conversation is due in November.
How this is solved: Warehouse cost nobody can trace to a dashboard or queryFriday, 14:00
Quarterly planning starts from a spreadsheet of 63 open requests collected from email, chat and corridor conversations, several of them the same ask in different words. The team also owes the business a move of the old store reports onto the agreed metric model, work only Priyanka has done before, and she leaves in six weeks. Two analytics engineers joined this month. Last quarter's plan promised eleven deliverables and landed five.
How this is solved: A data roadmap promised on skills the team does not have yetNone of this is unusual for a data team. The idea behind the solution is that each moment starts from the same agreed definitions and the same record of what loaded, who asked and who may see what, and that each product hands the next one something concrete instead of another spreadsheet or chat thread.
Challenges and how they are solved
Each challenge shows the problem as it happens, how the products are designed to hand work to each other, and the concepts that illustrate it. Share any challenge on its own.
Answers are in plain business terms. Choose Technical, or open any technical detail, to see how it works under the hood.
Challenge 1 of 6
Net sales reaches the weekly trading meeting in three versions, because each dashboard carries its own rules for shipping fees, discounts and returns. The analytics engineer already has a finance-aligned definition drafted. What keeps it in draft is that nobody can say which dashboards, saved reports, scheduled loads and chat assistants read the current rule, which of them will move or break, or by how much. So the change waits, the three figures keep circulating, and every attempt to fix one report risks quietly breaking another.
Leaders stop trusting dashboards, analysts spend their weeks reconciling figures by hand, and every change to how a number is calculated either stalls or quietly breaks a report somebody depends on.
SemanticFed keeps one agreed definition of net sales with a history of every change. Before a new rule goes live, it is designed to show which dashboards, scheduled tasks and assistants use the figure and how recent weeks would move, so the owner approves with the impact in view. FluidGrids then delivers the agreed figure to BigConsole on a schedule, so every board shows the same number.
Technical detail
SemanticFed holds net sales as one metric in a versioned semantic model, with the entities, dimensions and aggregation behind it, and is designed to record a named owner for it; the model moves from DRAFT to PUBLISHED, and publishing bumps its version (v4), so a new definition is a reviewable event rather than a silent edit. Before v4 is published, its change review is designed to list every saved query, BigConsole console, FluidGrids workflow and Botlit agent that reads the metric and to recompute recent weeks under both rules. Once the owner approves, a FluidGrids workflow is designed to run the saved query on a schedule and push the result into a BigConsole data sink by key. BigConsole's grounded generation is designed to name the sink, parser and fields behind every proposed widget, so a new board is bound to the published figure rather than to a private copy of the logic.
Designed so a change to how net sales is calculated is sized before anyone approves it, the trading meeting opens on one figure, and its definition, owner and change history are one click away.
SemanticFed
Opened from Propose change, it shows the proposed net sales definition beside the published one, every console, saved query, workflow and agent that reads it, and how recent weeks would move, before sign-off.
Works with BigConsole, FluidGrids, Botlit
BigConsole
Is designed to name the data sink, parser and fields behind every proposed widget, so a new trading board is bound to the published net sales figure instead of a hand-written copy of the rules.
Challenge 2 of 6
A vendor quietly changes the layout of a nightly export, part of the estate arrives with no rows, and the load job still reports success. The morning sales flash then shows a steep drop that never happened, and the dashboard has no way to say its numbers are incomplete. The first person to notice is often an executive who has already asked store managers what went wrong, and the data team spends the morning proving a negative instead of fixing the file.
Bad data reaches decisions before the data team knows it is bad, people learn to double-check every figure by phone, and the team's credibility takes the hit for a file it never saw.
FluidGrids checks each overnight till file against the agreed layout and what normally arrives, store by store, and holds everything back when a delivery comes in short. BigConsole is designed to show one board of which figures are late or incomplete and to put a clear warning on every dashboard that depends on them. SemanticFed flags a struggling source system early.
Technical detail
FluidGrids runs the overnight till load into the warehouse as a scheduled workflow whose validate step is designed to check the file's column layout against the agreed list and to compare row counts per store with the trailing four weeks; a shortfall or layout change fails the run with a typed error, and an error-trigger workflow alerts the engineer on call. Only a passing run is designed to let the next workflow run SemanticFed's saved net_sales query and push the result into the store_sales_daily BigConsole sink, whose status feeds a freshness board designed to hold each sink against a freshness target and an expected row range, list the consoles that read it, and put a plain banner on those consoles until the breach is cleared. SemanticFed's connection probes add source-level health, so a degraded source system shows up before its next load does.
Designed so the data team hears about a short or late load before an executive does, and every affected dashboard says so plainly while it is being fixed.
BigConsole
Holds every data sink behind the consoles against freshness and completeness targets, links each breach to the load run behind it, and flags the consoles that need a warning.
Works with FluidGrids, SemanticFed
FluidGrids
Lists every load run with its trigger, status and duration; a failed run opens node by node so the engineer can fix the mapping and retry only that step.
Challenge 3 of 6
Requests for customer data arrive by email or chat with no stated purpose or end date. Residency rules keep some records inside their home region and consent limits what they may be used for, yet approval often happens in a thread, and an extract of names and email addresses outlives the reason it was made. The data protection lead cannot see who holds which fields, for what purpose or until when, while the person asking just wants an answer this week.
Access requests either stall for weeks or get waved through by email, copies of personal data spread beyond the region they belong in, and nobody can answer an audit question from one place.
The analyst asks through SemanticFed, which is designed to show the approver which customer details are involved, where they are stored and why. FluidGrids routes the request to the right person, waits for a decision and puts an end date on access. Only members who agreed to marketing are included, the list stays in the EU, and the analyst sees counts only.
Technical detail
SemanticFed is designed to take the request against governed entities rather than raw tables: requester, purpose, fields and expiry sit on one review beside each source's region, and its planner pushes work down to the EU source so only counts or approved columns leave. Its PII proposals are designed to surface columns such as email and date of birth as inactive deny rules that only a person can activate. The review is designed to add a row rule limiting the list to members with marketing consent and to propose that the EU marketing team builds and sends the audience inside the EU while the requesting analyst receives segment counts on a BigConsole sizing console. A FluidGrids decision-rule set is designed to route each request by data class, region and purpose to the right approver, wait for the decision, then activate the approved policy with an expiry; every query run is attributed in the audit trail.
Designed so access is decided with a stated purpose and end date, personal records stay in their home region, and the analyst sizes the audience while the list itself is built and sent inside the EU.
SemanticFed
Puts requester, purpose, fields, consent, source region and the proposed grant on one review, with a counts-only option and an expiry, before anything is activated.
Works with FluidGrids, BigConsole
SemanticFed
Is designed to surface sensitive columns such as email and date of birth as inactive deny rules with their detection reasoning, which only a person can approve and activate.
FluidGrids
Holds decision logic as a versioned rule set tested against sample inputs; here it is designed to route EU personal data requested from outside the EU to the data protection lead.
Challenge 4 of 6
Every day brings a stream of routine questions to the team's ask-data channel: how returns are running at a group of stores, whether a product category is up on last year, why a conversion rate fell. Often the figure is already on a dashboard, but the person asking never found it or does not trust it. Analysts reply one by one while planned analysis waits, and figures pasted from public chatbots, traceable to no system, start circulating next to the real ones.
Analysts turn into a help desk, decisions wait in the queue, and answers with no source start circulating alongside the real ones.
Botlit is designed to answer routine questions in the team chat from the agreed figures and existing dashboards, showing where each number came from and how up to date it is. Answers built on the agreed figures respect what the person asking may see. When there is no agreed answer, Botlit says so, and FluidGrids passes the question to the data team's request list in PlanMagnet.
Technical detail
A Botlit agent on the chat channel is designed to answer from BigConsole consoles and SemanticFed's published metrics, citing the metric and model version, the console and filters it read, and when the underlying data sink last refreshed. Botlit agents can query dashboards and trigger workflows. Answers built on SemanticFed metrics are designed to run under the asker's own row, column and entity policy, so there the agent sees no more than the person asking; answers read from a console follow that console's sharing, so restricted data should reach the agent only through SemanticFed. When a sink is flagged late or incomplete, the agent says so instead of quoting it; when no published metric covers a question, it triggers a FluidGrids workflow designed to file it as a request in PlanMagnet. BigConsole's Explain panel is designed to handle why-did-it-move questions, with findings cited to sink, field and chart.
Designed so routine questions get a sourced answer in the channel, analysts get their mornings back, and unanswered questions become visible demand instead of lost messages.
Botlit
Answers chat questions with a card citing the metric, model version, console, filters and data freshness, holds figures from late sources, and routes unanswerable questions to the team.
Works with SemanticFed, BigConsole, FluidGrids
BigConsole
Is designed to give short findings on why a figure moved, each cited to the sink, field, parser or chart behind it, and to skip widgets whose source sink is stale.
Challenge 5 of 6
The cloud data warehouse invoice jumps from one month to the next, and finance asks what happened. The candidates are always the same: a new model's overnight jobs, a wall board refreshing every few minutes around the clock, exploratory work left running. The vendor's billing view breaks spend down by compute cluster only, so nobody can tie it to a team, a board or a recurring report, and the renewal gets negotiated without knowing which usage is worth its cost.
Data spend grows faster than anyone can explain it, the team cannot show which work is worth its cost, and the renewal is negotiated without knowing which usage is waste.
AdapterCloud is designed to tie the warehouse bill to the systems and teams behind it and to flag sudden jumps with an owner attached. SemanticFed is designed to show which recurring requests run most often and longest, and suggests cheaper ways to answer them, such as reusing a recent result. FluidGrids then refreshes busy boards only as often as people actually need.
Technical detail
AdapterCloud is designed to model the warehouse, lake storage and compute clusters as resources through its data-system adapters, attribute cost records to them and flag a month's jump on one cluster with its owner tag. That anomaly, carrying the cluster and date range, is designed to open SemanticFed's run history filtered to that source and window, listing the saved queries run there by frequency, duration and cache hits, with tuning proposals such as a longer cache lifetime that a person applies or dismisses. Work outside SemanticFed, such as a model's overnight jobs and ad-hoc sessions, is designed to appear in AdapterCloud as cost records on the cluster broken down by workload. A BigConsole board's cost comes from its feed, so the wall board fix is a FluidGrids schedule change: its feed runs every 30 minutes in store hours instead of every five minutes around the clock.
Designed so every rise in data spend arrives with the system, team and workload behind it, and savings come from specific, reviewed changes rather than a blanket freeze.
AdapterCloud
Attributes cost records to the resources they came from, such as warehouse clusters and storage, and is designed to flag run-rate jumps so a month's rise lands on a specific system.
SemanticFed
Is designed to read query run history back as proposals, such as caching a revenue query that runs dozens of times a day with no cache hits, which a person then applies or dismisses.
Challenge 6 of 6
Requests reach the data team through email, chat and hallway conversations, and by planning time the same ask has often been logged several times in different words. Platform work competes for the same people, and some of it depends on a skill only one engineer holds, sometimes an engineer who is about to leave. New hires are not ready for it yet, the last plan promised more than it delivered, and the requesters who were left waiting are the ones on the planning call.
Plans overcommit people who are already stretched, knowledge walks out with one departure, and the business comes to see the data team as a queue that never moves.
PlanMagnet is designed to collect every request for the data team in one list, merge the ones that ask the same thing and link each to the decision it supports. The plan is checked against who is free in CrewFoundry and which skills they hold. Skill gaps become hands-on learning in TechnoSpam, including sessions with the departing expert, so dates move with readiness.
Technical detail
PlanMagnet is designed to run a data request intake: requests from chat, email and forms, plus Botlit's unanswered questions filed through a FluidGrids workflow, land in its feedback inbox, where duplicate review merges the same ask. Each accepted item names the decision it informs, its owner and the date it is needed, so decision-intelligence work is ranked by that decision and delivered with its data date and model version. Accepted work becomes epics sized against analytics engineering capacity from CrewFoundry, through PlanMagnet's CrewFoundry integration, and matched to CrewFoundry's canonical skills graph, which shows the migration needs a skill one person holds. That gap is designed to become a TechnoSpam learning path, with mentor sessions booked with the departing engineer before she leaves, and path progress is designed to pass back to the PlanMagnet epic so its start date moves with readiness.
Designed so the data roadmap is committed against real capacity and skills, requesters can see where their ask stands, and skill gaps close before they turn into missed dates.
PlanMagnet
Captures each request with its business value and effort, and reviews suggested duplicate clusters before merging, so the same ask in different words becomes one item.
PlanMagnet
Plans the quarter's data epics against CrewFoundry capacity and the skills each needs, flags skills with a single holder, and moves start dates with TechnoSpam readiness.
Works with CrewFoundry, TechnoSpam, Botlit
CrewFoundry
Keeps one shared list of skills such as SQL, data modeling and metric design, with proficiency per person, so the plan can see who actually holds each one.
TechnoSpam
Shows a learner's progress across courses, practice problems, mentor sessions and certificates: the evidence a lead would use to judge who is ready for modeling work.
How it fits together
Each figure is defined once, delivered and checked on a schedule, shown on shared dashboards and answered in chat. What the data team cannot answer yet, and what costs too much, becomes planned work, checked against the people and skills available. Each step is one product doing one job and handing the next a concrete item.
To FluidGrids: The agreed figures, ready to be refreshed on a schedule
To BigConsole: Checked figures, with a note of anything late or short
To Botlit: Dashboard figures, with how up to date each one is
To PlanMagnet: Questions without an agreed answer, as new requests
To PlanMagnet: Cost jumps with the system and owner behind them
To CrewFoundry: Planned work and the skills it needs
To TechnoSpam: Skills the team is short of, person by person
Step 1 of 8: Define and govern
Technical detail
Products in this solution
Agreed metric definitions and who may see what
SemanticFed keeps one agreed definition of each business figure with a history of changes, and is designed to name its owner. It is also designed to decide who may see which customer details and keep personal records in their home region.
Technical detail
Federated query and a versioned semantic layer of entities, dimensions and metrics, with row, column and entity policy, lineage and audit designed to apply at the query edge. It is where a metric is defined once and where access and residency are decided.
Shared dashboards, freshness warnings and cited explanations
BigConsole is where people read the agreed figures on shared dashboards. It is designed to warn when figures are late or incomplete and to explain changes with the source behind each point.
Technical detail
AI-assisted, definition-first BI with keyed data sinks, parsers and consoles; each sink carries a ready, stale, refreshing or error status, and an Explain panel is designed to cite the sink, field and chart behind each finding. It is where the business reads the agreed figures.
Scheduled loads, completeness checks and hand-offs
FluidGrids collects figures from your systems on a schedule, checks each delivery is complete and raises the alarm when it is not. It also passes requests and approvals between the other products.
Technical detail
Visual workflow automation with schedules, validate and aggregate steps, error triggers and per-node run history. Its BigConsole data sink node is the built-in path for feeding consoles, and its workflows carry requests and approvals between products.
Cited answers in the team's chat channel
Botlit is designed to answer routine data questions in team chat from the agreed figures, show where each number came from, and say plainly when there is no agreed answer yet.
Technical detail
Enterprise agents on Slack and Teams channels that can query dashboards and trigger workflows, with an execution record of tokens and cost per answer. Here an agent is designed to answer ask-data questions from consoles and published metrics and to say so when nothing matches.
Warehouse and compute spend tied to owners
AdapterCloud is designed to tie the data warehouse and computing bill to the systems and teams behind it, and to flag sudden jumps with an owner attached.
Technical detail
Infrastructure inventory with data-system adapters and cost records attributed to the same resources, so data platform spend sits on the systems the team operates. Anomaly flags and per-team showback are designed to build on those records.
Request intake and the data team's roadmap
PlanMagnet is designed to gather every request for the data team in one list, merge repeats and build a quarterly plan that respects how much the team can actually take on.
Technical detail
Roadmaps, a feedback inbox with duplicate review, OKRs and sprints, with capacity-aware planning through its CrewFoundry integration. Here it turns scattered data requests into one ranked, capacity-checked plan.
Team capacity and skills
CrewFoundry shows who on the data team is available and which skills each person holds, so the plan does not depend on someone who is already stretched or about to leave.
Technical detail
People platform with a canonical skills graph, proficiency per person and resourcing views; it supplies the capacity and skills PlanMagnet plans against, so a date does not rest on someone who is overloaded or leaving.
Hands-on upskilling for the data team
TechnoSpam gives analysts and engineers hands-on lessons and practice in the skills the plan needs, and is designed to show the team lead who is ready for the work.
Technical detail
Learn-by-doing lessons, practice problems with instant feedback, mentorship and team learning paths, with a Data & Analytics skill track planned. It is where a skill gap becomes a plan the team lead can follow.
All eight products run on Burdenoff Workspaces: one sign-on, role-based access, one audit trail and one bill. The same identity is designed to decide what a person sees in a SemanticFed metric, a BigConsole console and a Botlit answer, with access decisions, loads and answers kept in one record.
Concept gallery
15 concepts from 8 products. Each one links to its own page on the product's website, and every view has a link you can share.
SemanticFed
Opened from Propose change, it shows the proposed net sales definition beside the published one, every console, saved query, workflow and agent that reads it, and how recent weeks would move, before sign-off.
Works with BigConsole, FluidGrids, Botlit
BigConsole
Is designed to name the data sink, parser and fields behind every proposed widget, so a new trading board is bound to the published net sales figure instead of a hand-written copy of the rules.
BigConsole
Holds every data sink behind the consoles against freshness and completeness targets, links each breach to the load run behind it, and flags the consoles that need a warning.
Works with FluidGrids, SemanticFed
FluidGrids
Lists every load run with its trigger, status and duration; a failed run opens node by node so the engineer can fix the mapping and retry only that step.
SemanticFed
Puts requester, purpose, fields, consent, source region and the proposed grant on one review, with a counts-only option and an expiry, before anything is activated.
Works with FluidGrids, BigConsole
SemanticFed
Is designed to surface sensitive columns such as email and date of birth as inactive deny rules with their detection reasoning, which only a person can approve and activate.
FluidGrids
Holds decision logic as a versioned rule set tested against sample inputs; here it is designed to route EU personal data requested from outside the EU to the data protection lead.
Botlit
Answers chat questions with a card citing the metric, model version, console, filters and data freshness, holds figures from late sources, and routes unanswerable questions to the team.
Works with SemanticFed, BigConsole, FluidGrids
BigConsole
Is designed to give short findings on why a figure moved, each cited to the sink, field, parser or chart behind it, and to skip widgets whose source sink is stale.
AdapterCloud
Attributes cost records to the resources they came from, such as warehouse clusters and storage, and is designed to flag run-rate jumps so a month's rise lands on a specific system.
SemanticFed
Is designed to read query run history back as proposals, such as caching a revenue query that runs dozens of times a day with no cache hits, which a person then applies or dismisses.
PlanMagnet
Captures each request with its business value and effort, and reviews suggested duplicate clusters before merging, so the same ask in different words becomes one item.
PlanMagnet
Plans the quarter's data epics against CrewFoundry capacity and the skills each needs, flags skills with a single holder, and moves start dates with TechnoSpam readiness.
Works with CrewFoundry, TechnoSpam, Botlit
CrewFoundry
Keeps one shared list of skills such as SQL, data modeling and metric design, with proficiency per person, so the plan can see who actually holds each one.
TechnoSpam
Shows a learner's progress across courses, practice problems, mentor sessions and certificates: the evidence a lead would use to judge who is ready for modeling work.
Pitch kit
Data teams lose their week in the gaps between tools: three versions of the same figure, loads that fail without a sound, access decided in email, analysts answering chat all morning, a warehouse bill nobody can trace and a request list nobody can see. Burdenoff brings eight products together, designed so each metric is defined once, checked on its way in, answered with its source and planned for by a team with the right skills.
Questions
No. SemanticFed is designed to read your figures where they already are, in your existing data warehouse and business systems, rather than copying everything somewhere new. BigConsole keeps the prepared figures its dashboards show, delivered by FluidGrids on a schedule.
Technical detail
No. SemanticFed is designed to query data where it already lives, in your warehouse, lake, operational databases and SaaS APIs, pushing filters and aggregations down to each source rather than copying tables. BigConsole data sinks hold the shaped results its consoles read, fed by FluidGrids workflows that connect to your systems. Copies are an opt-in optimization, not the architecture.
Not necessarily. Your data warehouse stays where it is, and SemanticFed is designed to let other reporting tools read the same agreed figures later on. Many teams would start with one gap, such as agreeing key figures or catching incomplete loads, and add more from there.
Technical detail
Not necessarily. The warehouse stays where it is and remains a source that SemanticFed queries in place. SemanticFed's planned SQL endpoint is intended to let other BI tools read the same governed metrics, so a team could keep an existing tool while BigConsole consoles and Botlit answers read the same definitions. Many teams would start with one gap, such as metric definitions or load checks, and extend from there.
SemanticFed is designed to work out totals inside the region where personal records are stored, so only approved totals or fields travel. The same access rules are designed to apply to people, scheduled tasks and assistants, and every use is recorded. It supports your own rules; it is not legal advice.
Technical detail
SemanticFed is designed to send each query to the source that holds the data, so rows from an EU source are filtered and aggregated inside that source and only approved columns or aggregates come back. Access policies scoped to rows, columns or entities are defined once and designed to apply to every query, whether a person, a scheduled workflow or an agent runs it, and every run is attributed in the audit trail. This supports your own residency rules; it is not legal advice and does not by itself make you compliant with any regulation.
No, not for answers built on the agreed figures in SemanticFed, which are designed to follow the asking person's own access. Answers read from a shared dashboard follow that dashboard's sharing, and finer controls there are still being built, so sensitive customer data should reach the assistant only through SemanticFed.
Technical detail
No, not for answers built on SemanticFed metrics: those are designed to run under the asker's own row, column and entity policy. Answers read from a BigConsole console follow that console's sharing, and BigConsole's per-viewer row rules and masking are still being built, so restricted data should reach Botlit only through SemanticFed. Answers cite the metric, model version, console and data freshness, and the agent says so when nothing published covers a question rather than inventing a figure. Every execution is recorded with its tokens and cost.
No. The products are pre-launch or early, and this page describes a solution concept; several screens shown are proposed designs. Three links are documented: FluidGrids sending figures to BigConsole, Botlit reading dashboards and starting tasks, and PlanMagnet planning around CrewFoundry's view of who is free. The other hand-offs show the intended design.
Technical detail
No. The products are pre-launch or early, and this page describes a solution concept. Several surfaces shown here, including the metric change review, the access request review, the freshness board, the analytics answer desk and the data roadmap view, are proposed designs; SemanticFed's federated query engine and policy enforcement, and BigConsole's Explain panel and field masking, are on their roadmaps. The documented integrations are FluidGrids data sinks feeding BigConsole, Botlit agents querying dashboards and triggering workflows, and PlanMagnet planning against CrewFoundry capacity; the other hand-offs describe how the products are designed to work together.
Wherever the pain is sharpest. Teams whose leaders argue over figures could start by agreeing their top few in SemanticFed. Teams hurt by incomplete loads might start with FluidGrids checks and BigConsole warnings. Teams buried in questions could start with a Botlit answer desk, and a PlanMagnet request list would follow from what it cannot answer.
Technical detail
Wherever the pain is sharpest. Teams whose leaders argue over figures could start by modeling their top metrics in SemanticFed and binding consoles to them. Teams burned by silent load failures might start with FluidGrids validation steps and the BigConsole freshness view. Teams buried in ad-hoc questions could start with a Botlit answer desk over existing consoles, and PlanMagnet intake would follow from the questions it cannot answer.
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A solution concept for B2B software companies: eight Burdenoff products that link what sales promises, what engineering ships, what customers ask in-app and who may churn.
Tell us about your setup. We will walk you through the products involved and scope a pilot around the challenge that hurts most.
This is a solution concept: it shows how Burdenoff products are designed to work together in this industry. The images are illustrations of the concepts, not screenshots of the actual products, and every name and figure in them is sample data.
