Generative AI · Enterprise generative AI
Your pilot generative AI runs on a public API. Your enterprise data, compliance team, and cost model say it can't stay there.
Banao builds enterprise generative AI systems that replace the shared API with a governed stack: your models, your data residency, your output format — measured against your actual workloads before they talk to a user or act on a record.
We cover the full scope: selecting or fine-tuning the right model, building the retrieval and grounding layer, wiring guardrails and approval logic, deploying into your own infrastructure, and handing over an evaluation suite that proves the system is working — so your AI team can run and extend it without coming back to us for every change.
Banao— Vikaas runs on a generative AI stack Banao developed and operates on its own 300-person demand-generation operation.
What enterprise generative AI development includes
An enterprise generative AI system is the model, the grounding layer, the guardrails, the governance, and the evaluation harness — we build all of them as one deliverable.
Foundation model selection and private deployment
Evaluating open-weight and licensed models against your task requirements, data-residency rules, and cost envelope, then deploying the selected model into your own cloud or on-premise environment — not a shared API.
Fine-tuning and domain adaptation
Adapting a base model to your vocabulary, output format, and task distribution using LoRA, QLoRA, or continued pre-training — so the model answers in your domain's terms rather than a general approximation of them.
Retrieval and grounding layer
A retrieval pipeline over your documents, policies, and internal data that grounds every generation in cited sources — and stops the model from interpolating facts it was never given.
Enterprise guardrails and content policy
Output filters, allow-lists, and policy enforcement built in code rather than relying on model discretion — so generated content meets your legal, HR, and brand standards before it reaches anyone.
Approval gates and human-in-the-loop
A sign-off layer on consequential AI outputs — contract clauses, financial summaries, customer-facing communications — so a person reviews what matters before the output leaves the system.
Evaluation and regression testing
A task-level evaluation suite built from your real prompt distribution, run before launch and after every change, so a prompt or model update cannot silently degrade accuracy.
Governance, audit trail, and cost controls
Logging of every prompt, retrieval context, output, and model version; a cost-per-query dashboard; and the attribution data your compliance, legal, and finance teams will ask for.
Multi-model routing architecture
A routing layer that directs routine generations to a fast, inexpensive model and hard or high-stakes outputs to a stronger one — controlling inference cost without trading off accuracy on the outputs that count.
Why enterprise generative AI fails at the governance layer, not the model layer
Enterprise generative AI projects that survive the pilot stage tend to fail at the same three points: the model answers with information it was never given, the output contains something the compliance team cannot approve, or inference cost climbs to a level nobody accounted for in the business case. None of these are model-selection problems. They are architecture and governance problems that need to be solved before any model touches a production workflow.
Banao designs the governance layer before the model layer. The retrieval and citation architecture, the content policy enforcement, the approval gates on consequential outputs, and the cost controls are specified and built as part of the system — not retrofitted after the pilot's failure modes surface in front of users.
Output accountability from day one
Every generation is logged with its prompt, retrieval context, model version, and output — so any problematic response can be traced, the attribution chain verified, and the fix narrowed to a specific component rather than a vague 'the model did it'.
Cost that stays inside the business case
We instrument inference cost per query from the start and build routing that reserves expensive model calls for outputs that need them — so the unit economics are known and manageable, not a bill you discover at the end of the month.
A system your AI team can run
We hand over the architecture documentation, the eval harness, and the prompt-and-model versioning that let your team retrain, re-route, or swap a model component without starting from scratch each time the underlying model changes.
Grounding: how enterprise generative AI avoids inventing answers
The most expensive failure mode in enterprise generative AI is confident invention: the model produces a plausible answer that cites a policy, a clause, or a number that does not exist. In a customer service workflow this produces misinformation. In a legal or financial context it produces liability.
Grounding solves this by connecting the model to a retrieval layer over your actual documents and data. Every generation is anchored to retrieved sources. Claims without a retrieved basis are suppressed or flagged. The output carries citations so the reader — and the auditor — can verify what the model used. Building the retrieval layer correctly — chunking strategy, embedding model, reranking, and fallback behaviour when nothing relevant is found — is as much of the project as building the generation layer itself.
Retrieval over your authorised corpus
We build the pipeline over your documentation, policies, contracts, and internal knowledge — not a generic web index — so the model retrieves relevant, authorised information rather than the closest-sounding public text.
Defined fallback behaviour
When the retrieval layer finds nothing sufficiently relevant, the system signals low confidence or declines to generate rather than filling the gap from the model's pre-training. Honest gaps are better than confident errors.
We built enterprise generative AI for our own operation before building it for yours
Vikaas is an enterprise generative AI system Banao developed and runs on its own demand-generation operation: it processes lead data, drafts and routes outreach, and tracks conversion — with retrieval grounding, output logging, and cost monitoring in place from day one. Every prompt, every output, every model version is logged and auditable. We depend on it across a ~300-person engineering business.
InterviewGod, our AI screening system, uses generative AI to read and rank applications against the role specification — with citations so a recruiter can verify every claim. Running both systems on our own operations means we have met the governance, grounding, and cost-control problems your team will face — not in a client engagement, but in our own operating budget.
- VikaasGenerative AI system Banao built and runs on its own demand-generation operation, live daily.
- InterviewGodGenerative AI Banao built and runs on its own candidate screening process every week.
When enterprise generative AI is the wrong investment
Generative AI earns its cost in a narrower set of situations than the market implies. We will tell you this before you commit a budget:
- The task is deterministic and well-structured: if a rule, a template, or a deterministic algorithm produces the correct output every time, a model adds latency and cost without adding accuracy.
- Volume is too low to justify the governance overhead: an enterprise generative AI system requires evaluation suites, logging infrastructure, and governance processes — overhead that is not worth carrying for a use case that runs a handful of times a week.
- You cannot provide sufficient grounding context: if your relevant documentation is sparse, fragmented, or unavailable for retrieval, the model will either hallucinate or produce output that cannot be verified — and grounding is the only reliable fix for that.
- Consequential outputs cannot be reviewed before they act: any generation that directly modifies a record, sends a communication, or executes a transaction without a validated approval gate carries risk that must be handled explicitly in the architecture before the system goes anywhere near production.
How we start — measure the gap before we design the system
We don't design a generative AI architecture off a brief. We run your hardest prompt distribution against candidate approaches first.
- AI Discovery Sprint2 weeks · fixed price
We take your actual prompt distribution, test candidate models and grounding approaches on your hardest inputs, measure output quality and cost per query, and hand back an architecture design, an evaluation framework, and ROI maths — yours to keep regardless of what you decide next. If you proceed, the Sprint cost is credited against the build.
- Build
We build the model layer, retrieval pipeline, guardrails, approval gates, governance logging, and evaluation harness — instrumentation and eval coverage are deliverables, not afterthoughts added at go-live.
- Production and continuous improvement
We ship with a cost-per-query dashboard and a live eval signal, retrain or re-tune as your workload evolves, and extend the system to adjacent use cases as measured performance earns it.
Frequently asked questions
What is enterprise generative AI development?
It is building a generative AI system fit for an enterprise environment: the right model or fine-tuned model, a retrieval layer grounded in your data, guardrails and content policy enforcement, approval gates on consequential outputs, governance logging, and an evaluation suite — all deployed in your own infrastructure so your data stays under your control.
How is enterprise generative AI different from calling a public model API?
A public API is the fastest path to a working demo and the slowest path to enterprise compliance. Enterprise generative AI development means data residency in your own jurisdiction, a model fine-tuned on your domain data, governance logging that satisfies your audit team, and cost controls that keep inference inside the business case. The model is one component; the architecture and governance around it are the project.
How do you prevent a generative AI system from producing incorrect or harmful outputs?
We build suppression in layers: retrieval grounding that anchors every generation to cited sources, content policy enforcement in code rather than relying on model behaviour, confidence thresholds that route uncertain outputs to a reviewer, and approval gates on any output that acts on a system or reaches a customer. The evaluation suite includes adversarial inputs designed to trigger each failure mode before the system goes live.
Can you deploy generative AI in our own cloud or on-premise environment?
Yes. We deploy on your cloud infrastructure — AWS, Azure, GCP, or private cloud — or on-premise where data must stay inside your environment. For GCC clients, that means UAE-region or Saudi-region deployment under the relevant data protection rules. We select and optimise the model stack to the target hardware and your latency requirements.
How do you control the inference cost of running generative AI in production?
We instrument cost per query from the start and build a routing layer that sends routine generations to fast, inexpensive models and hard or high-stakes outputs to the stronger model. We set a cost-per-query target in the Discovery Sprint and report against it in production — so inference cost is a line item you can manage, not a bill you discover at the end of the month.
How do you measure whether enterprise generative AI is performing correctly?
We build an evaluation suite from your actual prompt distribution — including edge cases and adversarial inputs — and score the system before launch and after every change. Metrics include output quality on your tasks, retrieval accuracy, cost per query, and the rate of outputs that required a human override. These are reported on a dashboard your team can monitor without asking engineering.
How long does enterprise generative AI development take?
A Discovery Sprint runs two weeks and produces the architecture design, evaluation framework, and cost model. A build engagement typically runs eight to fourteen weeks depending on the number of use cases, depth of the grounding layer, and integration complexity. Banao's engineering bench in Bangalore and Chandigarh means work starts in weeks, not the months a fresh internal hire would take to ramp.
Do we own the model, the pipeline, and the evaluation harness?
Yes. We hand over the fine-tuned weights or adapters, the training and fine-tuning scripts, the retrieval pipeline, the governance logging setup, and the evaluation harness. Your AI team can retrain, re-route, or extend the system without coming back to us for every change.
Show us where your current model keeps getting it wrong
Bring the prompt distribution, the output quality problem, or the governance requirement your current approach cannot meet. In 45 minutes we will tell you what an enterprise generative AI system would look like — and what it would take to build one that passes your audit team's review.
Book a 45-min scoping call