RAG & AI Architecture, done right

LLM and RAG systems that actually work — and don't bankrupt you. Proven retrieval patterns, honest cost modelling, and evals that tell you the truth. Senior AI architecture, not demo-ware.

What You Get

  • RAG and LLM systems designed around real retrieval quality, not a flashy demo
  • Honest cost modelling up front — know what inference will cost before you ship
  • Evals that actually measure quality, so you can tell regression from vibes
  • Privacy-first architecture — your data and your users' data stay yours
  • No vendor lock-in — swap models and providers without rewriting everything
  • Patterns proven in production (see LegalDeskAI), not slideware
  • Architecture-first: maintainable AI systems, not prompt spaghetti
  • Works with your stack and your model choice (open or hosted)
  • Direct access to a senior architect, not a junior reselling an API

Duration

Flexible (project-based or retainer)

Price

Custom (contact for a scoped quote)

Past the demo, into the hard part

Anyone can wire an LLM to a vector database and get a good demo. The demo is not the product.

The product is what happens when real users ask real questions: the model hallucinates on the ones that matter, retrieval surfaces the wrong context, the bill grows faster than usage, and a prompt tweak silently makes everything worse with no way to tell. That’s where most AI projects quietly stall.

What “done right” means

  • Retrieval you’ve measured. Not “it seems to find the right stuff” — actual numbers on what comes back.
  • Evals before opinions. A way to know whether a change helped, before you ship it to users.
  • Cost modelled up front. You should know what a thousand queries cost before you commit to an architecture, not after.
  • Privacy in the architecture. Sensitive data stays where it belongs, by design.
  • No lock-in. Models and providers sit behind boundaries, so you can swap them when the field moves.

Same AI everyone else is using. The difference is architecture that survives contact with production.

Proof, not slideware

We didn’t learn this from a course. LegalDeskAI is a private, multilingual AI system for Indian legal professionals — built to keep client data private while doing real retrieval and generation. That’s RAG under real constraints, in production.

If your AI system is already in trouble, that’s AI-Codebase Rescue territory — we untangle it and rebuild the parts that matter.

Let’s architect it

Bring the problem — the hallucinations, the cost, the retrieval that won’t behave — and we’ll design the system that holds up.

Book an architecture call.

Frequently Asked Questions

What makes a RAG system 'done right' versus a demo?

A demo answers the questions you chose. A real system answers the questions your users actually ask — including the ones that should return 'I don't know.' Done right means measured retrieval quality, evals you trust, modelled cost, and privacy built in. We architect for the second case, because that's the one that ships.

How do you keep AI costs from spiralling?

We model cost before you commit — token usage, retrieval calls, model tiers — and design the architecture to match your real query patterns. Often that means smaller models where they're enough, caching, and retrieval that doesn't over-fetch. You should know roughly what a thousand queries cost before you ship, not after the bill arrives.

Will we be locked into one model or provider?

No. We architect for portability — your prompts, retrieval, and evals sit behind boundaries so you can swap models or providers without rewriting the product. Open or hosted, your choice, and changeable later when the field moves (it will).

How do you handle privacy and sensitive data?

Privacy is an architecture decision, not a checkbox. We design data handling, retrieval, and storage so sensitive data stays where it should — we've built this for legal and health contexts where it's non-negotiable. See Regulated-industry AI for that work specifically.

Do you build it, or just advise?

Both. We can architect and build alongside your team for the hard parts — retrieval, evals, the cost-sensitive paths — or guide your engineers and review their work. Most engagements are a mix: hands-on where it's tricky, guidance for the rest.

Can you help fix an AI feature that's already in trouble?

Yes — that overlaps with AI-Codebase Rescue. If your RAG system already shipped and is hallucinating, over-spending, or unmaintainable, we untangle it and rebuild the parts that matter, rather than starting over.

Book an architecture call →