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Product engineering leads
Shipping an AI feature and finding that the last twenty percent of reliability is most of the work.
Fractional AI Architect / Anthropic
Building a product on Claude is easy to start and hard to make reliable. The architect works next to your engineer on the parts that decide reliability: context, tools, evaluation and cost.
Anthropic and Claude
Context and tool design
14 hrsPairing with your engineer
12 hrsEvals and guardrails
8 hrsDocumentation and handover
6 hrsThe difference between a demo and a product on Claude is rarely the prompt. It is which model in the family each call should use, what goes in the context window and what should not, how tools and MCP servers are defined so the model can actually use them, what your evaluation suite proves before a release, and whether the cost per task holds at real volume. Prompt caching alone can change the economics of a workload by an order of magnitude, and it has to be designed for rather than added later.
Who it is for
Product and engineering teams building on Claude who have something working and need it to be dependable.
Shipping an AI feature and finding that the last twenty percent of reliability is most of the work.
A demo that impressed everyone and a production bar it cannot yet clear.
Unit economics that worked at pilot volume and do not at real volume.
Coverage
The design decisions that separate a Claude demo from a Claude product, none of which are about prompt wording.
Which model in the Claude family fits each call, and where a smaller one is both cheaper and better.
What belongs in the window, what belongs in retrieval, and how prompt caching is designed for rather than retrofitted.
Tool definitions the model can actually use correctly, and MCP servers that expose your systems safely.
A suite that tests representative cases and gates releases, rather than a spreadsheet of vibes.
Input and output boundaries, refusal behaviour, and what must never be automated without a human.
Direct API against Bedrock against Vertex, chosen on data boundary, procurement and latency rather than habit.
Straight answer
We build on several model families. This is where we would and would not reach for Claude.
Outcomes
Representative cases, a pass bar, and a release decision that stops being a judgement call.
Model routing and caching designed so the unit economics survive real volume.
The same deliberate goal as every track: your team carrying more of it each month.
Working with Indian enterprises
Most of our engagements run with banks, NBFCs, insurers and manufacturers headquartered in India. The architect works IST, joins your existing rituals, and is used to the approval chain an Indian enterprise actually has.
Architecture reviews, vendor selection and security sign-off go faster face to face. The architect travels to your offices across the metros and tier 2 cities as the engagement needs it.
Data residency, consent and purpose limitation under the DPDP Act 2023 shape the architecture from the first session, alongside RBI, IRDAI and SEBI expectations where they apply.
Design decisions, model choices and control gaps are written down as you go, in a form audit and risk can read without a translation layer.
Where a workload cannot leave the country, the architect can design against open-weight models running entirely on Indian infrastructure through Gyde Inference.
Delivered onsite in
Free download
A sample engagement plan for a 40 hour month: what the architect does in week one, what your engineer owns by week four, and the artefacts that exist at the end of it.
Questions
If your question is here in a form we have not covered, ask us directly and we will answer it plainly.
No, and the distinction matters. AI Coding Agent Enablement is about rolling out Claude Code and similar tools to your engineering team so they write software faster. This engagement is about building your own product on Claude models. Different buyer, different work, and many organisations want both.
Whichever fits each call. Most production systems end up routing across the family rather than standardising on one model, using a larger model where reasoning quality decides the outcome and a smaller one everywhere else. Getting that split right is usually the single largest cost lever available.
It depends on data boundary, procurement and latency rather than on features. Bedrock suits teams already governed on AWS with an existing agreement, direct API suits teams who want the newest capabilities soonest, and the choice is worth making deliberately because switching later touches more code than people expect.
The Model Context Protocol is an open standard for exposing your systems and data to a model as tools, in a consistent way. You need it when the model has to act against internal systems rather than only generate text. Where it applies, it removes a great deal of bespoke integration code.
By building a suite of representative cases from your real workload, defining what a pass looks like before measuring, and wiring it into the release decision. Evaluation that is not connected to a release gate tends to become a report nobody reads, so the connection is the point.
Our senior AI architect holds the Claude Certified Architect Foundations credential, and the team builds on Claude in production for clients today. The engagement is staffed by people doing this work now rather than people who have read about it.
Yes, and the architect will be direct about the tradeoff. Where the DPDP Act 2023 or an RBI expectation means data cannot leave India, part of the design conversation is which workloads can use a hosted frontier model and which should run on open weights on Indian infrastructure instead.
That is common and usually correct. The architect designs the routing layer as well as the Claude-specific work, so a model can be swapped per task without rewriting the application. Our full-stack AI practice covers that routing layer in more depth.
Keep going
Start the conversation
Tell us the platform and the workload that is stuck, and we will propose a scope for the first 40 hour month.