AI consulting and implementation: strategy in a week, an agent in eight, governance you can audit.
Adopt and scale AI with confidence. AI strategy and adoption, AI implementation and automation, and AI agents and generative AI solutions, built directly on Anthropic’s Claude by an Anthropic Build Partner in Pune.
Decide, build, assure: the six pages behind this vertical
Each sub-service has its own page with the method, the calendar and the guarantee. This page is the map, in the order a buyer usually needs them.
AI strategy and roadmap, in one week
For teams that know they should be doing something with AI and do not yet know what. A scored opportunity map, a 12-month roadmap and a 90-day plan, with the executive readout on day five.
The one-week AI audit →BuildOne production AI agent in eight weeks
A single agent on your live data that recommends with evidence and acts on approval. Fixed scope, fixed price, and if it has not made or saved real money in 90 days, you do not pay for the build.
The eight-week build →BuildAI agents that watch the business
Reasoning agents with confidence scores, lineage and a kill switch on every signal, and a human deciding whether they act.
AI agents →BuildData agents on a governed layer
Agents that monitor metrics, explain what moved them, and with approval trigger the operation, built on the data foundation first.
Data agents →AssureAI evaluation
Graded evaluation sets written with your own experts, judge scoring with human review, drift monitoring and audit trails. An AI system cannot pass or fail; it has to be graded.
AI evaluation →AssureAI governance
Risk classification, bias and explainability evidence, privacy controls and automatic rollback, built into the pipeline the model ships from, assessed against the EU AI Act, NIST AI RMF and ISO 42001.
AI governance →Why ClaudeWhat Anthropic partner status changes on your project
Built directly on Anthropic’s models with no reseller layer, and the honest version of what the badge does and does not mean.
The Claude partner page →Where AI work goes wrong, and what we do instead
Four patterns from the work, each with a page or a written piece behind it.
A chatbot answers. An agent acts.
The build that pays for itself watches live data, catches what is costing money and acts once you approve. Answering questions is the demo, not the outcome.
Most of what gets scoped as an AI project does not need a model
A model earns its place on three conditions. A form, a rule and a lookup cover a surprising share of the backlog, and the audit says so when that is the answer.
The evals pass and users still say it is wrong
Offline sets encode what you imagined users would do. Live traffic is routed back into the frozen set so the suite gets harder over time.
The model changed underneath you
Behaviour drifts without a deploy. Logging every model call and grading the steps, not only the answer, is how a provider-side change becomes detectable.
Proof, with the customer’s own number on it
Two anonymised case studies and one named customer story, all in production.
Read the three
A margin agent that caught a $240k procurement leak in Week 3
What the agent watched, what it flagged, and who decided.
Read the retail case →Case study · Public sectorShipped AI agents the audit team approved on day one
Eight agents, eight weeks, and the controls that made the audit a formality.
Read the public sector case →Customer storyStockJarvis: a Claude intelligence layer inside a trading platform
Three phases in one month, from secure data layer to full rollout with training.
Read the StockJarvis story →How an AI engagement runs
The same three phases as every Woodfrog engagement, with the agent build on its own eight-week calendar.
- 01
Week 1, audit or scoping call
If you are not sure what to build, the fixed-fee audit or the one-week AI audit answers that. If you already know the agent you want, the first week is a scoping call and we size the build.
- 02
Weeks 2 to 8, build
Something real in front of real users by week three. A full agent build is live in eight weeks on a fixed scope, price and date.
- 03
Week 9 onward, operate
Evaluation in CI, drift monitoring, quarterly reviews and on-call governance. The people who built it pick up the phone.
What buyers ask about AI work
Do you do AI strategy consulting?
Yes. The one-week AI audit interviews six to ten stakeholders across leadership, operations, product, engineering and data, and leaves you with an opportunity map, a 12-month roadmap and a 90-day plan.
Do you do machine learning?
Machine learning is not a separate offer. Our AI practice is built on Claude: agents, evaluation and governance. Where pattern recognition earns its place inside a build, as it did in the StockJarvis research layer, it goes in.
What counts as a generative AI application here?
An agent or assistant on Anthropic’s Claude that does a job inside your systems, with evaluation sets, guardrails and a kill switch as standard. The eight-week build page describes the first one we would ship.
Which models do you use?
Claude Sonnet, Opus and Haiku, chosen per task, with model-selection guidance that comes with partner status.
How is our data protected?
The pattern we prefer is structured outputs only, which is how the StockJarvis build ran without exposing raw market data. Every signal carries its evidence and lineage, and governance is assessed against the EU AI Act, NIST AI RMF and ISO 42001.
What does it cost?
Fixed scope and fixed price, agreed after the first week, so there is a number before any build starts. If the agent has not made or saved real money in 90 days, the build is free.
Bring the use case, or bring the uncertainty
If you know the agent you want, talk to us and we will size it. If you do not, the one-week AI audit ends with exactly what to build first and what not to build at all.