LLM & RAG · LLM integration services
Banao integrates language models into the products and workflows your users already depend on — the API connections, the streaming layer, the fallbacks when a provider is slow, the caching that keeps the token bill from compounding, and the monitoring that tells you when a response has quietly degraded. We build the surrounding engineering, not just the prompt.
The model is a commodity. What is not is the production scaffolding around it — and the discipline to define exactly what the LLM should and shouldn't do before the first call goes out. We have built and run this scaffolding inside our own 300-person company; by the time an integration pattern reaches you, we have already had to trust it with our own work.
Banao— Vikaas, our own demand-gen engine, runs on an LLM integration we built and maintain for our own pipeline.
Calling an LLM API is the easy part. What makes the integration worth depending on is everything built around that call.
We connect the model provider to your codebase through its API or SDK — handling authentication, request formatting, token limits, and the edge cases a hello-world example never shows.
For chat-style and real-time features, we implement server-sent event streaming so the first token reaches the user in milliseconds, rather than waiting for the full response to buffer.
We build a routing layer that can shift a call to a secondary provider when the primary is slow or returning errors — so a bad API day at one vendor doesn't take down a feature.
System prompts, few-shot examples, and structured output schemas, tracked and versioned so a change to a prompt is a deliberate deployment, not an accidental edit in a config file.
We add a semantic cache that serves a stored answer to a sufficiently similar question without calling the model, and route simple tasks to cheaper models — keeping the token bill proportional to the work.
When a conversation grows past the model's context limit, we summarise or trim it in a way that preserves the facts that matter — so long sessions don't quietly lose track of what the user said ten messages ago.
We build graceful handling for API throttling and traffic bursts — queuing requests, applying exponential back-off, and surfacing progress to the user instead of returning a silent error.
Latency, error rates, token spend, and response quality, tracked in a dashboard, with alerts so a provider outage or quality regression is caught by your team before a user files a ticket.
A proof-of-concept integration has to work once, in the right conditions, with someone watching. A production integration has to keep working when the API is slow, when the model changes a behaviour after an update, when a user sends a 12,000-token message, and when a spike in traffic hits the rate limit at midnight with no one on call. The gap between those two things is almost the entire engineering job.
Teams that skip the gap often find out about it through a late-night alert, a complaint from a paying user, or a token bill that didn't match the forecast. We build the gap-filling work into the integration from the start — not as a cleanup phase after launch.
We wire in secondary providers and graceful degradation paths before launch, not after the first outage. A feature that falls back to a helpful message is better than one that returns a 500.
We route each call to the cheapest model that can handle it, cache answers to repeated questions semantically, and set spend alerts — so you can see the token cost against the business value it is generating.
System prompts are code. We version them, test them against a regression suite before they deploy, and track which version produced each logged response — so a model vendor's quiet update doesn't become a mystery customer complaint.
Latency and error rates are table stakes. We also track semantic quality — whether responses are staying on topic and within the boundaries you set — because a slow answer and a wrong answer are different failures.
A common mistake in early LLM integrations is sending every call to the same large, capable, expensive model. The result is a token bill that grows faster than usage, latency that surprises users on simple tasks, and a product that is more exposed than it needs to be to any one provider's pricing change.
We design an integration with a model-routing layer from the start — one that matches the weight of the model to the weight of the task. A complex reasoning step gets a capable model; a reformat or a classification gets a fast, cheap one. That layer also gives you a path to swap a model without rewriting the integration each time one provider raises prices or a better option appears.
We map your product's LLM calls by task type — generation, classification, summarisation, extraction, reasoning — and assign a model tier to each that clears the quality bar without overspending.
We build the integration against an abstraction layer, not directly against a single provider's SDK, so switching or adding a model is a configuration change, not a rewrite.
We set explicit latency targets for each call based on what the feature can absorb — a chat reply has a different tolerance than a background document summary — and choose models accordingly.
We attribute token spend by feature, not just by total, so you can see which product surface is generating value and which one is running an expensive model on traffic that could be served for a fraction of the cost.
Metrics shown dotted (··) are being finalised in our case-study metrics pack and will be published once verified. The integrations are live.
Studylab AI
We integrated the language model into Studylab AI's product with explicit context boundaries so the LLM answers only within the approved course material — with streaming responses and a prompt layer that enforces scope on every call.
Enterprise B2B SaaS platform (anonymized)
We built a multi-provider integration for a B2B SaaS platform's document-processing feature — routing calls by task complexity, caching answers to repeated queries, and failing over to a secondary provider on rate-limit or outage events.
Banao runs LLM integrations inside its own 300-person operation before any pattern reaches a client. Vikaas, our demand-generation engine, calls language models for outreach drafts and routes those calls through the same caching and fallback layer we build for clients. InterviewGod, our applicant-screening tool, sends structured queries to an LLM on every real hire, with the same prompt versioning and quality monitoring we deliver commercially.
That is not a story we tell to sell integration work. It is the test environment we use to find the failure modes that only appear at real volume — the slow provider day, the prompt that worked for three months then started drifting, the token cost that looked fine in staging and compounded in production. We find those inside our own systems first.
Not every product that could use an LLM should have one built from scratch. We'll tell you before you commission integration work:
We don't quote integration work off a brief. We map the calls, the edge cases, and the cost model first.
We map every LLM call your product needs, define the quality bar for each, model the token cost at target volume, and hand back a scoped integration design and build estimate — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We build the API layer, streaming, routing, caching, prompt management, and monitoring together — reliability engineering is a deliverable, not an afterthought added after the first outage.
We ship with quality monitoring and cost dashboards, update the integration as providers change, and keep the prompt suite tested against a regression harness so a model update doesn't quietly break a feature.
What is LLM integration?
LLM integration is the engineering work of connecting a language model to a product or workflow — the API connections, the prompt layer, the streaming setup, the fallbacks, the caching, and the monitoring that make the model's output a trustworthy part of your product rather than a one-off demo.
Which LLM providers do you work with?
We work across providers — Anthropic Claude, OpenAI, Google, and open-weight models for on-premises deployments — and we build the integration against an abstraction layer so you can switch or add a provider without rewriting. We recommend the right model per task rather than defaulting to whichever one you heard about most recently.
How do you handle provider outages or slowdowns?
We build a fallback path to a secondary provider before launch, not after the first outage. When the primary is slow or returning errors, calls route to the backup automatically. For features where degraded-but-available beats fully-down, we also design graceful degradation states.
How do you keep the API cost from growing uncontrolled?
Three mechanisms: model routing by task complexity (simple tasks get cheap models), semantic caching (similar questions reuse stored answers without a new API call), and per-feature spend dashboards so you can see which part of the product is generating the cost. We also set alerts before spend exceeds a threshold.
Can you integrate an LLM into our existing codebase?
Yes — we integrate into the codebase, language, and infrastructure you already have rather than asking you to adopt a new platform to add the feature. Most of the integration engineering is framework-agnostic and fits alongside existing code without a rewrite.
How do you monitor whether the integration is working well over time?
We track latency, error rates, and token spend as baseline telemetry, and we add semantic quality tracking — regular sampling of responses against a scoring rubric — so a drift in output quality shows up in a dashboard before a user notices it. Alerts fire on cost spikes, error-rate rises, and quality drops.
How long does an LLM integration take?
A two-week Discovery Sprint maps the integration and produces the design. A production build typically runs six to ten weeks depending on the number of call types, the streaming and caching requirements, and the monitoring depth. Our ~300-engineer bench means delivery begins in weeks, not months.
What is the difference between LLM integration and RAG?
LLM integration is the engineering layer that connects a model to your product — the API plumbing, routing, streaming, and reliability. RAG (retrieval-augmented generation) is a technique that gives the model knowledge from your own documents by retrieving the relevant passage before generating an answer. RAG usually sits on top of an integration layer; they are different scopes of work, and we often build both.
In 45 minutes we will walk through your use case, identify the reliability and cost questions a notebook demo doesn't answer, and tell you what a production-grade integration would take.
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