One agent that watches your business and acts on what matters
We build a single reasoning agent on Claude, running on your live data, in production in eight weeks. Every signal it raises carries its evidence, a confidence score and a kill switch, and a human decides whether it acts.
- Demo to production
- 8 weeks
- Terms
- Fixed scope, fixed price, fixed date
- No dollar outcome in 90 days
- The build is free
- Model layer
- Claude Sonnet, Opus and Haiku
- Team on the engagement
- A pod of three
What happens between a number moving and something being done
This is the path of one real signal, from the agent noticing a cost move to a person deciding.
- Watch
The agent reads live data continuously
Not a scheduled report. It monitors the sources you already run and holds the definitions your business agreed, so a move it flags means the same thing to Finance and to Operations.
- Detect
Delivered cost in APAC is up 12.4 per cent
Week 18. The agent raises it as a P1 signal rather than waiting for someone to open a dashboard and notice.
- Explain
Six sources, 91 per cent confidence
The signal arrives with the evidence behind it and the projected margin impact, minus 2.4 points. A number you cannot trace does not survive its first meeting with Finance.
- Recommend
Trigger a logistics RFP with DHL and Yamato
The agent proposes the action, not just the observation. Estimated saving $240k, first-year return nine times.
- Decide
Adopt, dismiss, or pull the kill switch
A person approves before anything runs. The kill switch is one click, on every signal, at every point.
What the agent is built to do
Six behaviours, in the order they usually get switched on.
Track performance metrics in real time
Continuous monitoring at production volume, up to 2,847 events a second on the reference agent.
Flag a signal that falls outside the norm
As it happens, rather than at the next reporting cycle, and with a severity attached to it.
Reveal what is driving a trend
The agent explores the data itself and surfaces patterns nobody thought to build a chart for.
Guide you toward the effective decision
A recommendation with the evidence and the confidence attached, in plain language, with no technical jargon.
Launch operations or campaigns
Purchase orders, alerts, campaign adjustments and workflows, each one behind a human approval step.
Hold a normal conversation
Ask it a question the way you would ask a colleague who happens to be connected to your data.
Where agents meet your internal tools →Figures from the reference agent
These come from one engagement rather than an average across clients, and we would rather say so than dress them up.
Eight weeks, and what happens in each of them
The shape is the same as every Woodfrog engagement, compressed to one agent.
- Week 1
Fixed-fee audit
Two calls and a one-pager you keep either way. It names the one decision the agent should serve and what your data can honestly support today.
- Weeks 2-6
Build
A pod of three connects the sources, writes the definitions, builds the agent and wires the approval and kill-switch path.
- Week 7
Operate
The agent runs on live data with humans on every action. Signals get adopted or dismissed, and the dismissals are the useful ones.
- Week 8
In production
Demo to production in eight weeks, on the fixed scope, fixed price and fixed date agreed in week one.
- Day 90
The outcome is measured
If the agent has not produced a measured dollar outcome by then, you do not pay for the build.
The agent gets more accurate because you use it
Accuracy on the reference agent over its first eight weeks, as it learned the questions the business actually asks.
Fewer reporting surfaces, not more
The dashboard audit on the reference engagement retired three surfaces and improved one.
What keeps an acting agent safe to run
An agent that can trigger a purchase order needs more governance than one that can only draw a chart.
Evidence on every signal
The sources behind a recommendation are listed with it. Six sources on the APAC signal, and you can read each of them.
A confidence score you can act on
Stated on the signal, not buried. A high-confidence signal and a marginal one should not be treated the same way, so the agent does not present them the same way.
Lineage and an audit trail
Any figure the agent quotes walks back to the record the source system sent. This is what makes a recommendation defensible after the fact.
Human approval before action
Adopt or dismiss sits with a person. The agent proposes, and nothing runs on your systems until someone with the authority says so.
A kill switch one click away
On the signal and on the agent, at any time. If the behaviour drifts, you stop it yourself rather than raising a ticket with us.
A governed data layer underneath
We build the data infrastructure the agent stands on, because plugging a model into ungoverned data is how you get confident wrong answers.
What one signal carries with it
The APAC cost signal, broken into the four things that arrive alongside it.
Where an agent is the wrong answer
There are cases where we would rather tell you in week one than take the build fee.
No decision is waiting on the other side of the answer
An agent earns its keep by shortening the distance between a signal and an action. If nobody would act differently on the answer, you are buying a faster way to feel informed, and a dashboard does that more cheaply.
Your data layer cannot support it yet
An agent is only as strong as the foundation it is built on. If your sources disagree and nobody owns the definitions, the honest first project is the data work, not the agent. That is a different engagement and it starts at /data-engineering.
Nobody in the business will accept a recommendation from software
Human approval and a kill switch are the right controls, but they only help if a person is willing to press adopt. If every action would go to a committee anyway, you have bought a slower report with extra steps.
You want a general assistant rather than one agent
This build is deliberately narrow: one agent, one decision, one measurable outcome in 90 days, described at /ai-use-case. If what you want is a broad copilot across everything, the guarantee cannot apply and we would not offer it.
You need a reasoning layer that is not Claude
We are an Anthropic Build Partner and we lead with Claude, choosing between Sonnet, Opus and Haiku by workload. We integrate the rest of your stack around it, but the reasoning layer is Claude. If that is a constraint, better you know now.
Three departments asking one thing
The silo is rarely the data, it is that each team has its own route to it.
- Finance: what was our burn rate in Q3?One agent, in natural conversation
- Operations: show me warehouse utilisation trendsOne agent, in natural conversation
- Marketing: which campaign drove the most sign-ups?One agent, in natural conversation
What buyers ask before they commit
The five questions that come up in almost every first call.
What exactly does the guarantee cover?
One reasoning agent, on your live data, in production in eight weeks, on a fixed scope, a fixed price and a fixed date. If it has not produced a measured dollar outcome within 90 days, you do not pay for the build. What counts as that outcome is agreed in week one during the audit and written down, so there is no argument at day 90 about the definition.
Which models does it run on, and what about the rest of our stack?
We lead with Claude and pick between Sonnet, Opus and Haiku per workload, as an Anthropic Build Partner and a member of the Claude Partner Network at launch. Around that we integrate the orchestration layer you already run, including Azure, Databricks, Snowflake and the BI tools your team trusts. Right model, right place, every time.
Do we need to build new pipelines first?
Usually not. The agent works on the data infrastructure you already have, which is why the first useful answers arrive in minutes rather than after a two to three week engineering sprint. Where the existing layer genuinely cannot support a trustworthy answer, we say so in the Week 1 audit instead of building on top of it and hoping.
How do you know the agent is right?
Every signal carries its evidence, its sources and a confidence score, and every number traces back through lineage to the record the source system sent. Beyond that, the agent is tested the way we test any model going into production. The method is set out at /ai-evaluation, and it is the part of this work we would want you to interrogate hardest.
What happens after week eight?
From week seven onward the engagement is operate: the agent runs, signals get adopted or dismissed, and accuracy improves as it learns the questions your teams actually ask, from 62 per cent in week one to 85 per cent by week eight on the reference agent. You can keep us on to run it or take it over. It is your agent, on your infrastructure. Past work is at /case-studies.
Eight weeks to production, ninety days to the measure
The build date and the measurement date sit inside one window, and the stretch between them is where the agent has to earn the fee.
The rest of the practice
These are genuinely different jobs with different ways of failing. Most engagements start in one of them.
- Data engineeringJobs that finish, migrations that reconcile, and storage that stops paying for cold data.
- Data integration and governanceOne set of records the finance team and the operations team both accept.
- Data platforms and modernisationOff the platform you outgrew, without a twelve-month freeze on new reporting.
- Apache SupersetSuperset built and run by people who commit to the project, including embedding and Kubernetes.
- Command centresThe one screen an operations floor runs the day from, not another dashboard.
- AI audit and roadmapOne week, and you know which two or three AI initiatives are worth building.
- AI evaluationA graded eval set, because an AI system cannot simply pass or fail a test suite.
- AI governanceGovernance you can defend in a board meeting and audit on demand.
- Data agentsAgents that watch the data, flag what moved, and explain what is driving it.
- AI use case, guaranteedOne production agent in eight weeks, or you do not pay for the build.
- Applications and automationThe system your team works in all day, built or replaced in slices.
- Application modernisationThe system nobody wants to touch, replaced a slice at a time rather than rewritten.
- System integrationSystems that stop disagreeing about the same customer, order and item.
- Process digitisationThe process that still runs on paper, WhatsApp and one shared spreadsheet.
Start with the audit, not the build
Two calls, a fixed fee, and a one-pager you keep either way: the one decision it should serve, what your data can support today, and the measured outcome at day 90. If an agent is not the right first move, we will say so.