AI · LLM & RAG

LLM and RAG development that grounds every answer in your own data

Banao designs and ships LLM applications grounded in your own documents and data through retrieval-augmented generation (RAG), so the model answers from your facts — with a citation a reviewer can check — instead of its training-time guesswork.

The model is the small part. The work is the retrieval that finds the right passage, the evaluation that proves an answer is faithful to the source, and the guardrails that decide whether it is trustworthy enough to show a customer. We build all three, and run the same stack inside our own 300-person company before any of it reaches you.

Banao— our engineers get cited answers from our own runbooks and codebase through an internal RAG assistant, every working day.

What we build into an LLM and RAG system

A grounded LLM application in production is a retrieval layer, an answer layer, an evaluation harness, and the guardrails that sit between them. We own the whole pipeline, not just the prompt.

RAG pipeline engineering

The full retrieval path — ingestion, chunking, embeddings, vector and hybrid search, and re-ranking — tuned so the model is handed the right passage before it ever writes a word.

Enterprise knowledge base AI

We connect the LLM to the systems your knowledge already lives in — wikis, SharePoint, ticket histories, PDFs, databases — so answers reflect your current truth, not a one-time export.

Vector search and indexing

Vector database selection and schema design, hybrid keyword-plus-semantic retrieval, and metadata filtering so results stay relevant as the corpus grows past the easy first thousand documents.

LLM fine-tuning and adaptation

When retrieval alone can't carry tone, format, or a narrow domain skill, we fine-tune — and we tell you honestly when it would add cost without moving the accuracy number.

LLM integration services

Wiring a model into the product and tools your team already uses — APIs, SDKs, streaming responses, and the fallbacks that keep a feature working when a provider has a bad day.

Hallucination control and grounding

Citations on every claim, confidence thresholds, and the discipline to say "I don't have that" rather than invent it — so a wrong answer is caught before a customer reads it.

Answer evaluation harness

Faithfulness and retrieval-quality scoring built from your real questions, run before launch and after every change, so accuracy is a measured number instead of a hopeful impression.

Guardrails, safety, and PII handling

Input and output checks, prompt-injection defence, and redaction of sensitive fields, so the system can read your private documents without leaking them into a reply or a log.

Model selection, routing, and cost control

The right model per step, with routing and caching, so a heavy reasoning task gets a capable model and a simple rewrite does not — keeping quality high without a token bill that outgrows the value.

Document ingestion and data freshness

Parsing, OCR, and de-duplication for messy real-world files, plus incremental re-indexing so a policy updated this morning is what the model retrieves this afternoon.

How we actually build a RAG system

Most of what decides whether a RAG system is trusted happens before the model is ever called. An LLM can only be as accurate as the passage it was handed; if retrieval returns the wrong paragraph, the most capable model in the world will write a fluent answer grounded in the wrong thing. So we treat retrieval as the product and the generation as the easy last step.

We start by mapping the real questions your users ask and the documents that actually hold the answers — which is rarely the tidy folder someone points us to first. From there the build is a sequence of measurable steps, each one scored against your own cases rather than a public benchmark.

Get the corpus right before the model

We parse, clean, and chunk your documents to match how they are written — a contract is split differently from a chat log — and attach metadata so retrieval can filter by product, region, or date instead of guessing.

Retrieve, re-rank, then generate

Hybrid search pulls candidates by both keyword and meaning; a re-ranker orders them by genuine relevance; only the top passages reach the model. Most accuracy gains we ship come from this layer, not from changing the model.

Ground the answer and cite the source

The model is instructed to answer only from the retrieved passages and to quote where each claim came from, so a reviewer can verify it in one click and the system can abstain when the source isn't there.

Score it before anyone trusts it

We run a faithfulness-and-relevance eval suite built from your real questions on every change, so you can see whether a tweak improved accuracy or quietly broke a case that used to work.

Why most RAG projects return confident, wrong answers

We get called in to fix RAG systems that demo beautifully and fail the moment a real user asks a real question. The failure is almost never the model being too weak — the models are strong now. It is the plumbing around them, and the same handful of mistakes repeat across nearly every stalled project.

We would rather name these on the first call than bill you to rediscover them on the third. If your retrieval-augmented prototype impressed everyone in the room and then quietly lost the team's trust, it most likely died of one of these.

Retrieval nobody measured

Teams obsess over the prompt and never check whether the right document was even retrieved. If the passage handed to the model is wrong, the answer is wrong — and no amount of prompt tuning fixes a retrieval miss.

Naive chunking

Splitting every document into fixed 500-token blocks cuts tables in half and severs a clause from the sentence that qualifies it. The model then answers from a fragment that means something different out of context.

No abstention path

A system that must always answer will always answer — including when the corpus has nothing relevant. Without a way to say "not found", the model fills the gap with a plausible invention.

A stale index

A pipeline indexed once at launch slowly drifts out of date as policies and prices change. The answers stay confident while the facts behind them quietly expire, which is worse than no system at all.

RAG, fine-tuning, or both — and what it plugs into

"Should we fine-tune our own model?" is the question we hear most, and the honest answer is usually "not yet, and maybe never." RAG and fine-tuning solve different problems: retrieval gives the model knowledge it didn't have, while fine-tuning teaches it a behaviour — a format, a tone, a narrow classification skill. Reaching for a fine-tune to fix a knowledge gap is a common, expensive detour.

For most enterprise problems, grounded retrieval over your live data gets you most of the way, and it updates the moment your documents do — no retraining run required. We add fine-tuning only where it earns its cost, and we build the whole thing to sit inside the stack you already run rather than beside it.

RAG for knowledge that changes

When the answer depends on documents that update — policies, pricing, product specs, tickets — retrieval is the right tool, because the system reflects the new version the instant it lands.

Fine-tuning for fixed behaviour

When you need a consistent output format, a house tone, or a domain-specific classification the base model gets wrong, a fine-tune earns its place — usually on top of RAG, not instead of it.

Wired into your systems

We connect retrieval to your real sources and the answer layer to your real products, behind your own auth and access rules, so a user only ever sees answers from documents they are allowed to read.

From proof-of-concept to production

A two-week proof tests feasibility on your hardest questions; the production build adds evaluation, monitoring, freshness, and access control — the parts a notebook demo never has to survive.

Grounded LLM systems already doing real work

Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.

Majra (UAE)

A national knowledge platform that answers from its own corpus

  • ··%of answers carry a source citation
  • ··smedian time to a grounded answer

We built an AI knowledge platform for the UAE's Majra that retrieves from its own published content and answers in both English and Arabic, with the source attached, so users get the organisation's position rather than a model's paraphrase of the open web.

Studylab AI

Learning answers grounded in the curriculum, not the open internet

  • ··%of responses traced to course material
  • ··×content coverage per learner

For Studylab AI we grounded the LLM in the approved course material so explanations stay inside the syllabus and cite the lesson they came from — which is what lets a teacher trust it in front of a class.

Enterprise services firm (anonymized)

Internal knowledge assistant over years of policies and tickets

  • ··%of staff questions self-served
  • ··minaverage research time saved

An internal assistant retrieves across a decade of policy documents and resolved tickets, answers with citations, and routes anything it can't ground to a named expert — so people stop pinging colleagues for facts already written down.

We run our own company on the LLMs we sell

Banao operates a ~300-person engineering company on its own LLM systems before any client sees them. Our engineers query their own runbooks, architecture decisions, and codebase through an internal RAG assistant; InterviewGod reads and evaluates applicant material with LLMs; Vikaas drafts grounded outreach for our own demand generation. All three run on real data, every working day, with our own people checking the output.

That is the difference between a vendor who has read about retrieval and one who depends on it to run a business. By the time a grounded LLM pattern reaches your workflow, it has already had to survive ours — including the boring, unglamorous failures that only show up at volume.

  • Internal RAG assistantAnswers our engineers from our own runbooks and codebase, with citations.
  • InterviewGodReads and evaluates applicant material before a recruiter opens the pile.
  • VikaasDrafts grounded outreach for Banao's own demand-gen pipeline.

Where we build and deploy LLM and RAG systems

We deliver from offices in India, the UAE, the UK, and the US, and we build retrieval and grounding to the data-residency and language rules each market expects.

GCC & UAE

From Dubai we build bilingual English-and-Arabic RAG for government and enterprise knowledge — including an AI knowledge platform for the UAE's Majra and long-standing work with RAK Ceramics. Retrieval and indexes stay inside UAE boundaries where the PDPL and client policy require it.

Saudi Arabia

Vision 2030 programmes need Arabic-first knowledge systems that keep data in-Kingdom. We build retrieval tuned for Arabic morphology and dialect, hosted to meet PDPL and SDAIA expectations for regulated workloads, so answers are both local and compliant.

United States

For California and New York enterprises we build internal knowledge copilots to SOC 2 controls, with the citation trail and audit logging US risk teams now require. The pull is cost: a grounded assistant deflects the research and support hours that have grown expensive to staff.

United Kingdom

Our Cambridge UK presence supports fintech and public-sector knowledge work under UK GDPR and ICO guidance, where every answer needs a source a reviewer can trace and a clear record of which document it came from.

India

Bangalore and Chandigarh hold our delivery bench, so a build starts in weeks. We design to the DPDP Act, handle multilingual corpora, and run cost-efficient delivery close to the engineering that ships it.

When an LLM or RAG system is the wrong tool

Most vendors will sell you a RAG build regardless. We would rather tell you when retrieval and a language model are the wrong shape for the problem — it is why technical teams take our second call.

  • Exact, deterministic lookups: if the answer is a single field in a database, query the database. An LLM adds cost and a small failure rate to a problem a SQL statement already solves perfectly.
  • A tiny, stable knowledge base: if the content fits on a page and rarely changes, a good search box or a written FAQ is cheaper and more reliable than a retrieval pipeline.
  • No source of truth: if your documents contradict each other and nobody owns the correct version, no retrieval system can ground an answer. Fix the data ownership first; the model can't.
  • Actions, not answers: if you need the system to update records or trigger a workflow rather than answer a question, that is agentic AI with guardrails, not RAG — a different build we will point you to.

How we start — prove the accuracy before you build

You have likely seen an LLM demo that impressed and a pilot that stalled. We start by proving, on your hardest real questions, whether a grounded system clears the accuracy bar your use case actually needs.

  1. AI Discovery Sprint2 weeks · fixed price

    We test retrieval feasibility on your real documents and hardest questions, then hand back a scoped RAG design, an evaluation plan, and the ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.

  2. Build

    We build the ingestion, retrieval, grounding, and the evaluation harness together — accuracy scoring and guardrails are deliverables, not afterthoughts bolted on once the demo is approved.

  3. Production & continuous accuracy

    We deploy with monitoring, incremental re-indexing, and a live eval suite, so the system stays current as your documents change and you can see its accuracy hold — or catch it the moment it slips.

Frequently asked questions

RAG is a pattern where, before the language model answers, the system retrieves the most relevant passages from your own documents and hands them to the model to answer from. It is how an LLM gives answers grounded in your current facts, with citations, instead of from its fixed training data.

RAG gives the model knowledge it didn't have by retrieving your documents at answer time; fine-tuning changes how the model behaves — its format, tone, or a narrow skill. Use RAG for knowledge that changes, fine-tuning for fixed behaviour. For most enterprise problems RAG does the heavy lifting and fine-tuning is optional.

Three layers. Grounding ties every answer to retrieved passages and cites the source; an abstention path lets the system say "I don't have that" instead of inventing; and an evaluation harness scores faithfulness on your real cases so regressions are caught. You can't reach zero, but you can make wrong answers rare, visible, and catchable.

Usually not, and often never. Fine-tuning a model to fix a knowledge gap is a common, expensive mistake — that is what retrieval is for. We recommend a fine-tune only when you need a consistent output format or a domain skill the base model gets wrong, and we'll show you the accuracy difference before you pay for it.

We are model-agnostic and choose per task, defaulting to the most capable Claude models for reasoning and grounded answering, and routing simpler steps to cheaper models. We build the retrieval and orchestration ourselves so you are never locked into a single provider or framework.

We deploy to your cloud and keep the documents, embeddings, and index inside the region your policy or regulation requires — UAE, Saudi Arabia, UK, US, or India. Sensitive fields are redacted before they reach a model, and access rules are enforced so a user only sees answers from documents they are allowed to read.

Yes — that is most of the real work. We parse and clean PDFs, scanned files via OCR, spreadsheets, and ticket exports, de-duplicate the contradictions, and chunk each format the way it is actually written. Messy source data is normal; we budget for it rather than pretend your corpus is tidy.

We build an evaluation suite from your real questions and known-good answers, then score both retrieval quality (was the right passage found?) and faithfulness (did the answer stick to it?). That suite runs on every change, so accuracy is a number you can watch over time instead of a feeling after a demo.

A common path is a 2-week Discovery Sprint to prove feasibility, a 6–10 week build, and a staged rollout that starts with a contained user group. Our ~300-engineer bench means delivery begins in weeks, not the months a fresh hire would take to spin up.

That is what the AI Discovery Sprint produces — fixed price, two weeks, a scoped design and an ROI model you keep whether or not you continue. Worst case you have a free, evidence-based assessment of whether grounded LLMs fit your problem; best case you have your board business case.

Find out whether a grounded LLM can answer your hardest questions

Bring the questions your team answers by hand from documents all day. In 45 minutes we'll tell you whether RAG can answer them accurately enough to trust — and what it would take to put one in production.

Book a 45-min scoping call