The margin leak the dashboard never showed.
A margin agent watching landed cost flagged a fuel-surcharge anomaly two weeks before close, recovering $240k while there was still a quarter left to fix it.
Read the margin agent case study →
Anthropic Build Partner · Built in Pune since 2023
Businesses now compete with data and AI.
Businesses now compete with data and AI.
We put them to work for you.
woodfrog brings data, analytics, and AI together to help businesses decide faster, run leaner, and perform better.
50+Projects delivered
20+Companies trust us
6 weeksMedian to first ship
8AI agents in production, zero audit gaps
Trusted by
What we do
Data, analytics, and AI under one roof.
From trusted data foundations to production AI, we help growing companies transform data into better decisions, smarter operations, and measurable business outcomes.
01 · Pipelines · Quality · Governance
Data Foundations
Build trusted data platforms that power analytics and AI.
- Data engineering & pipelines
- Data integration, quality & governance
- Data platforms & modernisation
02 · BI · Dashboards · Decisions
Analytics
Turn data into insights that drive better business decisions.
- Business Intelligence & reporting
- Dashboards & performance analytics
- Command centres & decision applications
03 · Strategy · Automation · Agents
AI
Adopt and scale AI with confidence.
- AI strategy & adoption
- AI implementation & automation
- AI agents & Generative AI solutions
04 · Applications · Modernisation · Integration
Digital Engineering
Modernise systems and build applications for growth.
- Business applications
- Application modernisation & migration
- System integration & process digitisation
Anthropic Build Partner
Production AI, built on Claude.
We build agents and applications directly on Anthropic’s Claude models, no reseller layer, no toy demos. A working relationship that gives our customers early access, model-selection guidance, and production patterns that pass an audit on day one.
AI already running for customers
+2.4ppAPAC margin recovered in 90 days by an agent watching landed costThe margin agent case study ↗
$240krecovered when that agent flagged a fuel-surcharge anomaly two weeks before close
- Lineage, policy checks, confidence scores and a kill switch on every signal
- A graded evaluation set before anything ships, and an on-call governance pod after
- Your data is never used to train a model, ours or anyone else’s
Selected work
Real business problems we solve
We help growing companies improve visibility, reduce manual work, lower operating costs, and make better decisions using data, applications, and AI.
01 / 08
All case studies ↗
Nobody will sign off on the AI.
Lineage, policy checks, confidence scores and a kill switch on every signal, plus an on-call governance pod. Eight agents in production, zero audit gaps.
Read the AI governance case study →Every team reports a different number.
Finance, sales and ops each pull their own extract, so the meeting starts by arguing about the data. We consolidate to one governed source with lineage you can audit.
Ask us about this →Decisions wait on last night’s batch.
The warehouse refreshes overnight, so Monday’s call runs on Friday’s numbers. We rebuild the pipelines so events land in minutes, not overnight.
Ask us about this →240 dashboards and still no answer.
A 240-board estate on a six-figure BI licence. Migrated to embedded Superset and cut to the 8 boards people actually decide from, saving $72k a year in seat fees.
Read the Superset migration case study →Account managers find out too late.
Churn signals sat in three systems nobody read together. Persona-driven views and an account-review queue lifted enterprise CSAT in one quarter and held $480k of ARR.
Read the churn signals case study →The real work still runs on spreadsheets.
The system of record says one thing, the spreadsheet and the WhatsApp group say another. We build the application the process actually needs and wire it back in.
Ask us about this →The legacy system blocks every change.
Ten years of custom logic nobody wants to touch. We modernise it in slices, keep it running throughout, and integrate rather than rip and replace.
Ask us about this →
How we work
Six weeks to something running in production.
No twelve-month roadmaps. We find the one decision costing you the most, and we fix it end to end.
Working days
Week 1Fixed feeWeek 2Week 3Week 4Week 5Week 6Week 7onward
AuditTwo calls, your estate mapped, a written one-pager you keep either way.
One-pager
BuildFive weeks, a pod of three, in slices. Something a real user can open by week three.
First delivery
DeployEach slice goes live as it is ready, wired to the systems around it.
Running in production
ObserveWatch the number move, adjust, and let the impact prove itself.
OperateQuarterly reviews, on-call governance, agent maintenance.
Fixed scope. Fixed price. Data, analytics and AI on a calendar, not a roadmap.
We measure our own ROI.
Every engagement carries a measured outcome, saved dollars, recovered margin, hours avoided. You see it in your CFO’s report, not just our case study.
We ship in weeks, not slides.
No 12-week strategy phase. By Week 3 something is in production. By Week 6 it’s measuring its own impact. Fixed scope. Fixed price.
We leave when you’re stronger.
We don’t run a staffing business. Our retention model is governance and on-call, not bodies-on-seats. When your team can fly solo, we step back.
Our office
Proudly based in Pune, India.
Founded here in 2023, and now working from our own office in the city, delivering across India, APAC and the US. We like visitors. Write first and we will send directions, because visits are by appointment.
From the writing
Notes from inside the AI work.
None of it written without doing the work first. The newest three on AI are below; everything else, including the older favourites, is on the writing page.
All writing ↗AI assuranceLogging every model call is the cheapest governance you will buyWhen an AI feature goes wrong, only the call record can tell you what happened. The hard part is not logging it, it is deciding what to keep and what to mask.AI assuranceThe model changed underneath you and nothing in your release notes says soBehaviour drifts without a deploy, and the first person to notice is a customer. What it actually takes to make provider-side change detectable.AI evaluationAn agent has to be graded on the steps it took, not only the answerA correct answer can hide a wrong tool call, a guessed parameter and a lucky input. Grading the steps costs more, and it is the part that predicts next month.
Asked, answered
Questions growing teams usually ask us.
Most companies do not start with a perfect setup. We help from where the business is today.
Which AI models do you build on?
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Do we need a data team already?
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Can Excel stay?
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Do you only do data work?
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Can you work with our existing tools?
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Do you also train the team?
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Can AI be added later?
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Do we need to change everything at once?
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What does this cost, roughly?
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How much of my own team's time does this take?
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What happens if it does not work out?
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What happens after the six weeks?
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Ready when you are
Start with one business problem.
Tell us where the business is losing time, margin, clarity, or control. We will help you understand what can be improved with data, analytics, applications, or AI.
hello@woodfrog.tech
A person replies within one business day
Pune, India · visits by appointment
Companies served20+
AnthropicClaude Partner Network
EngagementsNDA-friendly · fixed scope