Generative AI development · Generative AI integration
Your generative AI pilot passed the demo — now it has to survive production
Banao integrates large language models into the systems your business already runs: your CRM, your document store, your ticketing platform, your data warehouse. We wire the API, ground the model in your facts, build the eval pipeline, and put cost controls in place before the first live user hits it.
Integration is not a prompt and an API call. It is the retrieval layer, the output checks, the model routing, the latency budget, and the observability stack — all delivered as production code your team can operate.
Banao— Vikaas, our own demand generation system, runs on a generative AI integration we built and depend on every week.
What a Banao generative AI integration includes
Each integration is a production system, not a wrapper. We build the retrieval, evaluation, guardrails, and observability alongside the API wiring.
Model and API gateway wiring
We connect the right model to your stack — OpenAI, Anthropic Claude, Google Gemini, or open-source via AWS Bedrock, Azure OpenAI, or Google Vertex AI — with authentication, rate-limit handling, and failover in place.
Retrieval-augmented generation (RAG)
We ground the model in your documents, product data, policy files, and knowledge bases through a retrieval layer that returns citations, so outputs are traceable to your facts and not the model's priors.
Prompt architecture and versioning
We design and version your prompt templates as code — with unit tests, regression tracking, and a clear path to changing them without breaking downstream behaviour.
Output evaluation and quality gates
Before a generated response reaches a user, we run it through automated checks: factual grounding, format compliance, PII detection, and your domain-specific quality criteria.
Latency and cost controls
We implement caching for repeated queries, route simple tasks to cheaper models, batch where the latency budget allows, and monitor cost per call so your monthly bill is predictable.
System of record write-back
Where the integration should do more than display text — summarise a ticket and update the CRM field, generate a report and push it to the data warehouse — we wire the output to your live systems with the approval gates that write-back requires.
Guardrails and safety layers
Output filters, refusal handling, PII redaction before storage, and topic constraints — defined against your compliance requirements and enforced in code, not left to the model's built-in defaults.
Observability and tracing
Full trace of every prompt, retrieved chunk, and completion so your team can audit what the model said, why it said it, and what document it came from — required for any regulated use case.
The gap between a generative AI integration that works once and one that works every time
A proof-of-concept integration is easy to build: one model, one prompt, one clean input, one impressive output. The difficulty starts when inputs are ambiguous, the model produces a policy that does not exist, a retrieval miss returns nothing useful, or the API bill exceeds what the use case justifies.
We build integrations for the second condition, not the first. The retrieval layer, the eval suite, the output checks, the cost routing, and the observability are the integration — the API call itself is the short part.
Grounded in your data, not the model's training set
Retrieval-augmented generation means the model answers from your current documents, not from what it learned at training time. When your policy changes, the integration picks up the new version without a model update.
Evaluated before it reaches users
We build an eval suite from your real inputs — including the awkward, ambiguous, and adversarial ones — and run it on every deployment. A prompt change that degrades quality is caught in CI, not in a support ticket.
Cost that stays in the budget
We design the model routing and caching architecture upfront, so the integration does not start cheap and become expensive as usage grows. You know the cost profile before you go live.
Integration patterns across enterprise stacks
Enterprise generative AI integration is not a single pattern. The right architecture depends on where the output goes, how fresh the grounding data needs to be, what latency the user experience can tolerate, and what your compliance team requires about data residency.
We have built integrations that sit inside customer support platforms, internal knowledge tools, document review workflows, and data pipelines that generate structured outputs from unstructured inputs. Each pattern has different retrieval, evaluation, and cost characteristics — we choose the one that fits your workload.
Customer-facing generation
Draft responses, product descriptions, support answers. Requires the tightest output quality gates and the clearest refusal handling — outputs reach customers and carry brand risk.
Internal knowledge and search
Employees querying the integration about HR policy, product specs, or engineering docs. Requires strong retrieval and citation so the answer is auditable.
Structured extraction from documents
Contracts, invoices, applications, reports. The model reads unstructured text and produces a structured record the system of record can ingest. Evaluation checks field-level accuracy, not fluency.
The integrations we trust most are the ones we run ourselves
Vikaas is a generative AI integration Banao built and runs on its own demand generation: it drafts outreach, personalises content by account, and feeds qualified pipeline to our sales team — every week, across a ~300-person engineering operation.
InterviewGod, our hiring tool, grounds every candidate summary in the actual application and role requirements before a recruiter sees it. Building integrations we stake our own business on is a different standard from shipping one and moving on.
- VikaasA generative AI integration Banao built that runs our own demand generation.
- InterviewGodGrounds candidate summaries in real application data before any recruiter sees them.
Where we build generative AI integrations
India
Delivery runs from Bangalore and Chandigarh with a ~300-engineer bench, so integrations begin in weeks. Data residency for Indian clients complies with DPDP Act requirements.
UAE and GCC
From Dubai we deliver integrations for GCC enterprises with data kept inside UAE boundaries where the PDPL and sector-specific policy require it.
US and UK
For US and UK clients we build to SOC 2 and UK GDPR expectations, with the audit logging and data handling their legal teams require of any system that processes customer-facing text.
When a custom generative AI integration is not the right answer
We will tell you before you commit a budget to an integration that will not return it:
- An existing product already does it: if a SaaS tool with a built-in AI feature covers your use case, buying a seat is cheaper and faster than building an integration.
- The volume is too low to justify retrieval infrastructure: if the integration will process ten queries a day, the retrieval and eval architecture adds complexity that a simpler approach would not.
- The quality bar is higher than any current model can meet: some domains require factual accuracy that no retrieval-augmented integration can guarantee today. We will say so rather than ship something that fails in production.
- Fine-tuning is not the answer to bad grounding: teams often request fine-tuning when the real problem is that the model lacks access to current data. Fine-tuning trains on historical data at a point in time; RAG retrieves current data at query time. We will tell you which problem you have.
How we start — scope before we build
We do not quote a generative AI integration off a brief. We run a scoped test against your real data and use case first.
- AI Discovery Sprint2 weeks · fixed price
We scope the integration, test retrieval quality against your actual documents, evaluate model options on your real inputs, and hand back an integration design, eval plan, and cost model — yours to keep. If you proceed, the Sprint cost is credited against the build.
- Integration build
We build the retrieval layer, API wiring, prompt architecture, output evaluation, guardrails, cost controls, and observability stack — production code, not a notebook.
- Production and continuous improvement
We deploy behind a quality gate, monitor cost and output quality in production, and iterate on retrieval and prompts as your data evolves.
Frequently asked questions
What does 'generative AI integration' actually mean?
Connecting a large language model to your existing product or internal systems so it can read your data, generate text or structured outputs, and optionally write results back to your system of record — with evaluation, guardrails, cost controls, and observability built in. The API call is the short part; the integration is everything around it.
Which LLM providers do you work with?
We work with the major API providers — OpenAI (GPT-4o and variants), Anthropic (Claude), Google (Gemini) — and open-source models hosted on AWS Bedrock, Azure OpenAI, Google Vertex AI, or self-hosted infrastructure. We recommend the model based on your use case, cost tolerance, and data residency requirements, not on a preferred vendor relationship.
How do you make the model answer from our data, not its training set?
Retrieval-augmented generation (RAG): we index your documents, product data, and knowledge bases into a vector store, and at query time we retrieve the most relevant chunks and pass them to the model as context. The model is instructed to answer from the retrieved content and to say when it cannot find the answer. Outputs cite the source document.
How do you prevent the model from producing wrong or harmful outputs?
Multiple layers: output evaluation runs automated checks — factual grounding, format compliance, PII detection — before a response reaches the user. Guardrails add topic constraints and refusal handling. For write-back integrations we add approval gates on any action that modifies a system of record. Every prompt and completion is traced so problems are diagnosable.
How do you control the cost of generative AI at scale?
We design cost controls into the architecture from the start: semantic caching for repeated or near-identical queries, model routing that sends simple requests to cheaper models, batching for workloads where latency is not tight, and per-call cost monitoring. We build the cost model in the Discovery Sprint so you know the budget before you go live.
Can you integrate with our CRM, ERP, or internal databases?
Yes. We connect the integration to your existing systems through their APIs or direct database access, including older systems that require retrofit connectors. The integration reads from and, where appropriate, writes back to your systems of record — with the same approval gates you would apply to any process that changes live data.
How long does a generative AI integration take to build?
A common path is a 2-week Discovery Sprint, then a 6–10 week build, then a staged rollout beginning with internal users. Simpler integrations — a single retrieval pattern, one content type, one output format — can reach production faster. Banao's ~300-engineer bench means the build starts in weeks, not the quarter a new hire would take to ramp.
What is the difference between integration and fine-tuning?
Fine-tuning trains the model on your examples at a point in time — useful for style adaptation or task formatting, but it does not give the model access to data that was not in the training set. Integration with RAG retrieves current data at query time — useful when accuracy depends on up-to-date facts. Most enterprise use cases need integration, not fine-tuning. We will tell you which one your problem requires.
Bring the integration your team has been deferring
In 45 minutes we will tell you whether the use case is worth building, what the grounding and eval architecture would look like, and what production deployment would cost.
Book a 45-min integration scoping call