Gen AI Development
Generative AI features your users actually feel.
LLM-powered product features — RAG, copilots, search, generation, and summarisation — built with the right model (including the latest Claude models), grounded retrieval, guardrails, and evaluation, then shipped into your product and measured against real use.
Discuss this serviceRight-fit
Models per use-case
Grounded
Retrieval-backed
Measured
Evaluated before launch
Primary stack
Our primary stack — we're not limited to it. We pick the right tools for each project and pick up new ones fast.
Engineering led by Jatin Vaishnav
Outcomes you’ll actually feel.
Lower token cost
We cut LLM spend with right-sized models, prompt and context trimming, caching, batching, and retrieval instead of stuffing context — we benchmark cost per request and drive it down.
Grounded, not guessing
RAG and evaluation keep outputs accurate and on-brand, so users actually trust the feature.
Right model per job
We match each task to the right model — including the latest Claude models — for the best quality / latency / cost trade-off.
Shipped and measured
We launch a focused feature, benchmark cost and latency, and improve against real usage — no science projects.
Everything you need, nothing you don't.
Use-case & model selection
Pick the right model per use-case, balancing quality, latency, and cost.
RAG & retrieval
Ground responses in your data with retrieval that actually works.
In-product features
Copilots, generation, and summarisation built into your UX.
Guardrails & evals
Evaluation and safety so features hold up with real users.
How gen ai development runs.
Define
Choose the use-case and success criteria.
Build
Implement retrieval, prompts, and product UX.
Evaluate
Test against real examples before launch.
Ship
Release, measure, and improve.
Tangible deliverables, not slideware.
- Use-case & model selection
- RAG / retrieval pipeline
- In-product AI feature
- Guardrails & evaluation harness
- Cost & latency benchmarks
Gen AI Development, answered.
Right-sized models, trimmed prompts and context, response caching, batching, and retrieval over context-stuffing. We measure cost per request and optimise it — often cutting spend substantially without hurting quality.
Ready to talk gen ai development?
Tell us who you need to reach. We'll show you how data-driven demand generation turns into sales-ready leads.