Prove it. Scale it. Optimize it.
- Prove
Measure value, not activity
Tokens and seats show AI is busy. Bloomfilter shows whether the business got faster, cheaper or better: cycle time, rework, cost per outcome, by team.
- Scale
Redesign processes for humans and agents
Integrate human and agentic work into one governed workflow. See where agents carry the work, where people approve and where rework appears, so process change scales with an audit trail.
- Optimize
Put the next dollar where it returns most
Route each task to the model with the best outcome per dollar. The context graph improves evals and training data for fine-tuning, so inference gets better and cheaper as you scale.
Customers
Swantide
Partners
Works with
Initial frontier investments were justified by excitement. Growth will be justified by data.
Board mandate
Productivity improvement is a reported commitment now, not an experiment.
Cost pressure
Token spend is visible to the dollar and rising faster than payroll savings.
Time to market
The promise was acceleration. Quantifying that requires business context.
Cost is measured to the penny
Priced per million tokens. Attributed to team, model, day. Auditable.
Value requires business context
Increased revenue from higher productivity. Reduced total cost of delivery. Inferred from process systems.
Integrating the context allows data-informed decisions
The context layerOne process model between your AI and your business
Bloomfilter reads agent activity and your systems of record, stitches them into one event log, and measures every step in time, rework and dollars.
Frontier AI
The frontier model
Claude knows the work it did. Not the result it produced.
Sessions, prompts, tool calls, tokens and cost, by team and by model.
Whether that work shipped, held up in production or moved a business metric.
Every session is tied to the ticket, change and outcome it served, so model spend is priced against value.
The frontier model
ChatGPT knows the work it did. Not where that work went.
Conversations, custom GPTs, agent runs, tokens and cost, by workspace and team.
Which of that work reached a ticket, a release or a customer, and what it was worth.
Each workspace is tied to the processes it touched, so enterprise seats and API spend are priced against outcomes.
The frontier model
Gemini knows what it answered. Not what it changed.
Requests, tokens and cost across Workspace and the API, by project and model.
Whether faster drafting and analysis shortened a process or just added output.
Gemini usage is linked to the work it fed, so model choice follows cost per outcome.
The assistant
Copilot knows what it suggested. Not what that suggestion cost later.
Seats, active users, suggestions offered and acceptance rates.
Whether accepted suggestions shortened cycle time or created rework weeks later.
Adoption is linked to delivery outcomes team by team, so seat renewals rest on evidence rather than acceptance rates.
The agentic IDE
Cursor knows how fast code was written. Not whether it was the right code.
Agent runs, edits, the models used and time spent in the editor.
What that code cost downstream in review, rework and incidents.
Agent-written code is traced through review, release and production, so speed is weighed against quality.
IngestIllustrative data
Every event, from every system, as it happens.
Connectors read raw events from work trackers, code, service desks, ERP, CRM and agent telemetry. Deployed in your data cloud, they read in place and your data stays in your account.
Events ingested, last 30 days
- 11.2Mevents ingested
- 5systems connected
- <15mdata freshness
- 0records leaving your account
StitchIllustrative data
One unit of work, followed across every system it touched.
A ticket, the agent sessions that worked it, the code it produced, the release that shipped it and the incident it caused are joined into a single case.
Case PAY-4471: one feature, five systems
- JiraTicket PAY-4471
- Claude2 agent sessions
- GitHubPR #882, 3 commits
- CI/CDRelease r-10.3
- ServiceNowINC-221 rework
- 94%of events matched to a case
- 5different keys resolved to one case
- 1rework loop no single system could see
NormalizeIllustrative data
Four schemas become one language of work.
Every state change is mapped to a shared process ontology: one row per step, with the actor, the time and the fully loaded cost.
Raw, in four schemas
jira: issue_key, status_to, updated_atgithub: pr_number, event, merged_atservicenow: sys_id, state, assigned_toagent: session_id, tool_call, tokensOne event log: 1 row = 1 state change
| case_id | activity | actor | cost_usd |
|---|---|---|---|
| PAY-4471 | refine_requirement | human | 218.40 |
| PAY-4471 | implement_change | agent | 3.06 |
| PAY-4471 | review_change | human | 96.75 |
| PAY-4471 | rework_change | agent | 4.11 |
- 212 to 18raw activity names mapped to standard steps
- 100%of steps classified human or agent
- $ / stepfully loaded cost on every row
MeasureIllustrative data
Cost and value, in the same units, at every step.
Cycle time, rework and cost per outcome by team, model and process, each number traceable to its source record.
- Cycle time31h22% vs baseline
- Rework rate11%5 pts vs baseline
- Cost per outcome$36414% vs baseline
- Agent share of steps38%21 pts this quarter
Cost per step, case PAY-4471
HumanAgentAgents carried two steps for $7. The human steps around them, refinement and review, are where the next dollar of AI would return most.
OptimizeIllustrative data
Route every task to the model that earns it.
Outcomes by model and task show where a cheaper or open model matches frontier results on your own work. The same outcome-labeled history becomes evals and training data, closing the loop toward self-optimizing processes.
Step implement_change: outcome against cost by model
Outcome bar: 94% first-pass acceptance
| Model | Accepted | Cost per step | Verdict |
|---|---|---|---|
| Claude Opus | 96% | $3.06 | |
| GPT-5 | 95% | $2.41 | |
| Fine-tuned open model | 95% | $0.38 | Route here |
| Gemini Flash | 81% | $0.22 | Below bar |
- 01RouteSend each task to the cheapest model that clears the outcome bar.
- 02Fine-tuneOutcome-labeled examples and evals, drawn from your own process history.
- 03Close the loopOutcomes become the reward signal for processes that optimize themselves.
- −88%model cost on the routed step
- 95%acceptance held, frontier parity
- 18koutcome-labeled examples a month
Runs in your data cloud
Deploy Bloomfilter inside your data cloud or private environment and it writes the process model back as governed tables. Code moves in. Your data stays in your account. SaaS is available when that fits better.
Data platforms
Systems of record
Select a system to see what it can, and can't, tell you on its own.
The customer
Salesforce knows what it earned. Not what earned it.
Pipeline, orders, cases and renewals: the outcomes customers actually feel.
Which delivery, which capability and which AI investment moved those outcomes.
The loop closes from feature shipped to revenue retained, so AI is judged by what the customer received.
The run
ServiceNow knows what broke. Not what caused it.
Incidents, changes and requests: the operational pulse of the business and how quickly issues are resolved.
Which release, which change or which agent introduced the problem, and whether AI triage actually lowered the cost per case.
Every incident is traced back to the change and the work behind it, so the true cost of speed becomes visible.
The money
SAP knows what it cost. Not what produced it.
Invoices, purchase orders, cost centers and payments: the financial record finance trusts.
Which process step, which team and which AI investment drove the spend, or saved it.
Human and agent work are priced in the same dollars finance already recognizes, process step by process step.
The plan
Jira knows what was promised. Not what it was worth.
Every commitment, priority and status change: what was planned, what moved to done and how long it sat waiting.
Whether a person or an agent did the work, what it cost, and whether it reached a customer or came back as rework.
Each ticket is tied to the code, the agent sessions, the release and the business result it served, priced step by step.
The build
GitHub knows what was built. Not why it mattered.
Commits, pull requests, reviews and merges, including the growing share written by coding agents.
Whether the code solved the right problem. More pull requests can look like progress while rework quietly climbs.
Code activity is linked to the plan it fulfilled and the incidents it caused, separating real output from motion.
The data cloud
Snowflake governs the data. Not what the work achieved.
The governed enterprise record: tables, lineage and access policies, and increasingly agent identity and activity.
Whether the processes running on that data got faster, cheaper or better. Governance is the foundation, not the outcome.
Bloomfilter runs inside your Snowflake account, turning governed tables into a governed process model with outcome metrics.
The lakehouse
Databricks unifies the data. Not the story it tells.
Pipelines, models, experiments and the raw event history landed from every system you run.
How those events connect into one flow of work, and which AI investment changed that flow.
Bloomfilter reads the lakehouse in place and stitches events into an end-to-end process model. Your data stays where it is.
The Microsoft data estate
Fabric unifies the data and the semantic layer. Not the process flow between them.
OneLake, semantic models and Copilot across the Microsoft estate, often alongside Azure DevOps, GitHub and Copilot for developers.
How work actually moves from ticket to release to business result, and which agent investment changed it.
Bloomfilter joins OneLake data into one end-to-end process model, so Copilot and other agents can query outcomes, not just usage.
Every system is honest. Every system is partial.
Jira knows what was planned. GitHub knows what was built. ServiceNow knows what broke. SAP knows what it cost. Salesforce knows what it earned. Each gives a faithful, one-dimensional view, and none of them can say whether your AI investment made the business better. That answer only exists in the flow between them.
The enterprise that wins the next decade won't judge AI by the health of its tech stack. It will see its business as one continuous flow, from idea to invoice and from request to revenue, with people and agents in the same picture.
Bloomfilter is built to see that whole flow.
In production at Fortune 500 enterprises
Agentic process re-engineering
- developers
- 9,000+
- teams
- 900+
- systems
- 80+
- KPIs measured
- 15
- Re-engineer an AI-native product development lifecycle
- Score every team bronze, silver or gold on agentic usage and outcomes
- Accelerate adoption against that baseline, team by team
AI capital allocation
- developers
- 8,000+
- systems
- 10+
- process variants
- 20+
- KPIs
- 15
- Quantify the ROI of agentic spend as time-to-market acceleration
- Price the cost and value of tokens inside each business process
- Optimize model selection on evidence rather than preference
Agent governance
Invoice receipt through payment on SAP and a Salesforce-based AP platform. Mixed human and agent execution across extraction, validation, three-way match, and exceptions.
- A control tower over human and agent execution across AP
- Straight-through processing rate measured and improved against target
Hard to build, and it compounds
A frontier model can read any one system. It can't tell you how work actually moved across hundreds of teams, or what it was worth.
- Messy, sparse data
- Why it's hardNo two Jira, SAP or ServiceNow setups match. Work skips steps, moves backwards, and records go unlinked across systems.
- What we builtStitching that resolves human and agent events into one case, even as source schemas change.
- A process per team
- Why it's hardHundreds of teams run hundreds of variants of the same process: different stages, different handoffs, different definitions of done.
- What we builtAutomatic process discovery for every team, rolled up from task to initiative to strategy.
- Moving bottlenecks
- Why it's hardAutomate one step and the constraint moves to the next: an approval, a review, a handoff. The target keeps moving.
- What we builtMetric definitions and bottleneck patterns learned across every deployment.
- Context that fits
- Why it's hardA Fortune 500 context graph is far too large for any agent's context window.
- What we builtA context agent that gives each person and agent only the slice it needs.
A Board number can't change when you ask again.
Probabilistic answers are fine for a first draft. They are not fine for the figure that decides next year's AI budget. Bloomfilter computes every metric from the event log with versioned definitions, so the same question returns the same number and every number traces back to source records.
Models sit on top to explain and query the results. They don't invent them.
- Ask the same question twice
- A model on its ownTwo different answers
- On the context layerThe same number, every run
- Where did the number come from?
- A model on its ownIt can't show its work
- On the context layerTraced to source records
- What counts as cycle time?
- A model on its ownInferred from the prompt
- On the context layerVersioned, auditable definitions
- Years of history across hundreds of teams
- A model on its ownOverflows the context window
- On the context layerServed as the slice each question needs
One number finance will accept. In thirty days.
Three systems, five teams, ten metrics. Bounded, dated and built on data you already have.
Connect
Week 1: integrate three systems of record and your AI telemetry.
Discover
Week 2: map how work actually flows, human and agent.
Measure
Week 3: baseline cycle time, rework and cost per outcome.
Review
Week 4: walk finance through the value figure and where to invest next.