AI · LLM & RAG
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
"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.
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.
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.
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.
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.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Majra (UAE)
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
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
What is RAG (retrieval-augmented generation)?
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.
What is the difference between RAG and fine-tuning?
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.
How do you stop an LLM from hallucinating?
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.
Do we need to fine-tune our own model?
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.
Which LLMs do you build on?
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.
How do you keep our data private and in-region?
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.
Can it work over our messy, legacy documents?
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.
How do you measure whether the answers are accurate?
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.
How long until a RAG system is in production?
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.
How do we prove ROI before committing budget?
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.
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
