LLM features that survive real users .
LLM and RAG implementation for SaaS and product teams: retrieval pipelines, agents, and model integration built by engineers who run these systems in production. We have shipped RAG with evals, observability, and multi-model routing, not a demo that falls over on the tenth query. Quote-only, scoped to the system.
A prompt in a notebook is easy. The hard part is retrieval that stays accurate, agents that do not go off the rails, and evals and observability so you can trust the thing in front of customers. We build LLM features to be operated, because we operate them, and we write about how at length.
The LLM stack, done to a production bar.
Real retrieval, real evaluation, real observability. The parts that decide whether an AI feature earns trust or quietly erodes it.
RAG and retrieval
Chunking, embeddings, and retrieval tuned for accuracy on your data, with the evals to prove it holds up as the corpus grows.
Agents and tool-calling
Agents that call your tools and APIs safely, with guardrails and scoped permissions, not an open-ended loop you cannot predict.
Model integration and routing
Claude, OpenAI, or open models, wired into your product with multi-model routing so you are never locked to one vendor or price.
Evals and observability
Evaluation harnesses and LLM-aware observability so you can measure quality, catch regressions, and see what the system actually did.
How an LLM engagement runs.
We scope LLM work like the engineering it is: architecture first, evals throughout, no black boxes.
Discovery and architecture
A short paid discovery to scope the system, the data, and the failure modes, and produce a real architecture and estimate before you commit to the build.
Build with evals from day one
We build the feature and the evaluation harness together, so quality is measured from the first slice, not guessed at before launch.
Ship, observe, and operate
We deploy with observability in place and can keep operating it, or hand over a documented system your team can run. No lock-in.
How engagements are priced
LLM features are systems, not list-price products, so we scope before we quote. Pricing in USD. INR billing with GST available for India clients.
Discovery & architecture
- System and data architecture
- Failure-mode and eval plan
- A real build estimate
- Build engagements from $25,000
Embedded AI engineer
- Senior AI engineer inside your team
- RAG, agents, evals, observability
- 9am to 1pm PT daily overlap
- Month to month
Pricing in USD. INR billing with GST invoice available for India-based clients, ask on the call.
Straight answers.
What are LLM integration services?+
Building large language model features into your product: retrieval (RAG), agents, and model integration, with the evals and observability that make them reliable. We do it as engineers who run these systems in production.
What is RAG and do we need it?+
Retrieval-augmented generation grounds an LLM in your own data so answers stay accurate and current. You need it whenever the model must answer from your documents, product, or knowledge base rather than general training.
Which models do you work with?+
Claude, OpenAI, and open-source models, chosen per use case. We build multi-model routing so you can switch on quality or cost without re-architecting, and you are never locked to one vendor.
How do you keep an AI feature from going wrong?+
Evals and observability from day one. We measure quality against a test set, add guardrails and scoped tool permissions for agents, and monitor behaviour in production so regressions surface fast.
Can you work with our existing engineering team?+
Yes. We can scope and hand over a documented system, or embed a senior AI engineer inside your team on your stack, from $6,000 per month, with a daily PT overlap.
Who we build LLM features for
- SaaS companies adding an AI feature that has to be reliable in front of customers
- Teams with a promising prototype that now needs retrieval, evals, and observability
- Product teams that want AI added where it earns its place, not forced in
- Founders who need senior AI engineering without a long, expensive hire
Tell us the AI feature you're trying to ship.
A short call with the engineers who build these systems. We will pressure-test the idea, flag the hard parts, retrieval, evals, cost, and tell you the honest path to something you can trust.