AI · Generative AI
Banao builds generative AI systems that produce text, code, images, and structured data grounded in your own facts, tuned to your voice, and checked before any of it reaches a customer. We turn a general model into one that writes, drafts, and generates the way your business actually needs — with the evaluation, brand, and IP controls that make the output safe to publish.
The model is the easy part. The work is the grounding that keeps it accurate, the evals that prove it stays accurate, and the governance that keeps generated content on-policy and clear of copyright and confidentiality risk. It is the same stack we run inside Banao to generate our own marketing and screen our own hires before any of it reaches you.
Banao— Vikaas generates and sequences our own marketing content in production, every working day.
Generative AI in production is not one prompt. It is grounding, the right model for the job, an evaluation harness, governance, and the cost control to run it at volume — we own all of it.
Generation tied to your documents, policies, and records through retrieval, so the model writes from your facts with citations rather than its training data. It is the single biggest lever on whether generated output can be trusted.
When prompting and retrieval aren't enough, we fine-tune on your data so the model holds your terminology, format, and house style — and we measure whether the tuning actually beat a well-built prompt before you pay for it.
Systems that draft, edit, and version long-form copy, product descriptions, reports, and campaigns in your voice — with human review gates and an audit trail of what was generated and approved.
Internal copilots and code-generation pipelines wired to your repositories and standards, so generated code follows your conventions, passes your tests, and is reviewed like any other commit.
Text-to-image and text-to-video models tuned on your brand assets to produce product imagery, ad creative, and short video — with the rights and brand-safety checks that make the output usable, not just impressive.
Generated, privacy-preserving datasets to train and test downstream models where real data is scarce, sensitive, or imbalanced — with validation that the synthetic set behaves like the real one.
Models that turn messy inputs into schema-valid output — filled forms, drafted contracts, populated reports — with validation so a generated field is never silently wrong.
Versioned prompts, templating, and multi-step orchestration managed as code, so a generation feature is testable and reproducible rather than a string someone pasted once and forgot.
Task-level eval suites scored on your real cases — accuracy, faithfulness, tone, safety — run before launch and on every change, so a prompt tweak can't quietly degrade what your customers read.
Output filters, brand and tone checks, and provenance tracking so generated content stays on-policy, on-brand, and clear of copyright and confidentiality risk before it ships.
A foundation model out of the box writes fluent, generic text. The gap between that and output your business can publish is the whole project: it has to know your facts, sound like you, stay inside your policies, and be checked before anyone sees it. We close that gap with four layers, applied in order of cost.
We start with prompting and retrieval, because grounding a strong general model in your own data solves more problems than people expect — and costs far less than training. We reach for fine-tuning only when we can show it beats a well-built prompt on your own evaluation set. The aim is to spend your budget where it changes the output, not where it looks impressive on a slide.
Retrieval over your documents and data is the first lever we pull. It fixes most factual errors and keeps answers current without the cost and staleness of baking knowledge into model weights.
We fine-tune for the voice, format, and domain terms a prompt can't reliably hold — and we prove the lift on your eval set first. Tuning that doesn't beat a good prompt is cost without benefit.
Style guides, tone rules, and banned-term lists become checks in the pipeline, so generated copy reads like your brand wrote it rather than a generic assistant.
Nothing generated reaches a customer without passing the output checks and, where it matters, a human review gate. The default is to flag, not to publish on a guess.
A generation demo has to produce one good paragraph on a friendly prompt. A production system has to produce thousands a day, stay on-brand on the inputs no one anticipated, keep its token bill inside the value it creates, and give you a record of what it wrote. That is where the engineering lives.
We build the generation flow, then wrap it in the parts that make it safe to run at volume: evaluation, observability, cost control, and governance. The model is a component; the system that keeps its output correct, affordable, and on-policy is what you are actually buying.
Model routing, caching, and length control so generating at scale stays inside a budget you set, instead of a token bill that quietly outgrows the feature it pays for.
Logged prompts, outputs, and scores, so when a result is wrong you can see why and fix the cause rather than regenerate and hope.
Generation pushed into the tools your team already uses — CMS, CRM, support desk, repositories — through their APIs, so the feature gets adopted instead of admired.
Every generated artifact carries who or what produced it, from which sources, and who approved it — the provenance your legal and brand teams will ask for.
We get called in to rescue stalled generative-AI projects often enough to see the same causes repeat. The model is almost never the problem. The problem is grounding, evaluation, governance, and the economics of running generation at scale.
We would rather name these on the first call than bill you to rediscover them on the third. If your last generative-AI pilot never shipped, it most likely died of one of these.
Teams blame the model for making things up when the fix is grounding and retrieval. Without it, every generated answer is a guess your customer might catch.
Without an eval harness, quality is a matter of opinion. Teams tune prompts by feel, regress silently, and lose the confidence to ship.
Brand, IP, and confidentiality checks added after launch are the ones that fail in public. They belong in the pipeline from the first commit.
A feature that's cheap in a demo can be ruinous at production volume. Cost control is a design decision, not a post-launch surprise.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Banao — Vikaas
Vikaas drafts, sequences, and personalises Banao's own marketing content as a grounded generation pipeline, with a person approving what goes out. We generate our own demand engine on it before we offer the pattern to a client.
Majra (UAE)
For Majra, the UAE's national CSR and sustainability authority, we built generation and knowledge tooling that drafts and unifies content across the organisation in English and Arabic, grounded in its own approved sources and held to its editorial standards.
B2B SaaS platform (anonymized)
A grounded generation system drafts release notes, help-centre articles, and in-app copy from the product's own changelog and docs, with every draft reviewed before publish. Writers edit and approve instead of starting from a blank page.
Banao runs a ~300-person engineering company on the generative AI it sells. Vikaas generates and sequences our own demand-generation content; InterviewGod generates the screening and assessment material that filters our own engineering applicants. Both run on real systems, every working day, with our own team reviewing the output.
That is the difference between a vendor who has read about generative AI and one whose own marketing and hiring depend on it. By the time a generation system reaches your workflow, it has already had to hold up inside ours.
We deliver from offices in India, the UAE, the UK, and the US, and we build generation systems to the data-residency, language, and content-governance rules each market expects.
From Dubai we build bilingual English–Arabic generation for enterprise and government, including long-standing work with RAK Ceramics and content tooling for the UAE's Majra. Systems are built to keep data inside UAE boundaries where the PDPL and client policy require it.
Vision 2030's content, media, and digital-government programmes need Arabic-first generation that respects local dialect and context. We build on Arabic-capable models, validate output with native reviewers, and keep data in-Kingdom to meet PDPL and SDAIA expectations.
For California and New York enterprises, generated content now passes legal and brand review for copyright, disclosure, and accuracy. We build to SOC 2 controls with the provenance tracking and output governance US risk teams ask of any system that publishes on the company's behalf.
Our Cambridge UK presence supports fintech and public-sector content under UK GDPR and ICO guidance, where provenance, accuracy, and a clear human-accountability trail on anything published are non-negotiable.
Bangalore and Chandigarh hold our delivery bench, so a build starts in weeks. We generate across English and Indic languages, design to the DPDP Act, and run cost-efficient delivery close to the engineering that ships it.
Most vendors will fit generative AI to any brief. We would rather tell you when it doesn't earn its place — it is why technical buyers take our second call.
You have likely been pitched generative AI by several vendors already. We start by proving which of your outputs a model should generate, and which it shouldn't, before quoting a build.
We map your candidate use cases, build a grounded prototype on the hardest one, and hand back a scoped design, an evaluation plan, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We build the generation flow, grounding, fine-tuning where it pays, the evaluation harness, and the governance and cost controls together — evaluation and governance are deliverables, not afterthoughts.
We deploy behind review gates with full logging and cost monitoring, track output quality on live cases, and keep improving the prompts, retrieval, and models as your content and data change.
What is generative AI development?
Generative AI development is the engineering of systems that produce new content — text, code, images, or structured data — grounded in your own information and wired into your tools. It covers the retrieval, fine-tuning, evaluation, and governance that turn a general model into one your business can publish from, not a single API call to ChatGPT.
How is this different from just using ChatGPT or a chatbot?
A chatbot answers questions in a window. A generative AI system produces work your business ships — drafts, reports, code, imagery — grounded in your data, held to your brand and policy rules, and checked before it goes out. The model is one part; the grounding, evaluation, and governance around it are what make the output safe to use commercially.
Should we fine-tune a model or use RAG?
For most cases, start with retrieval (RAG): grounding a strong general model in your data fixes accuracy and keeps it current at a fraction of the cost. Fine-tuning earns its place when you need a consistent voice, format, or domain vocabulary a prompt can't hold — and we prove it beats a good prompt on your own evaluation set before recommending it.
How do you stop generative AI from making things up?
Hallucination is mostly a grounding problem, not a model defect. We tie generation to your documents and data through retrieval, add citations and confidence thresholds, validate structured output against a schema, and route anything the system can't produce reliably to a person. The default is to flag, not to publish a guess.
Who owns the generated content, and is it safe to use commercially?
You own the system, the prompts, and the outputs, and we deploy on model endpoints whose terms allow commercial use. We add brand-safety and provenance checks, train image and video models on assets you have rights to, and keep a record of sources so your legal and brand teams can sign off on what's published.
Will our data be used to train a public model?
No. We architect deployments so your data stays in your environment, use enterprise model endpoints that don't train on your inputs, and back it with encryption, access controls, and audit logging. Where policy or regulation requires it, generation runs entirely inside your own cloud or region.
How do you measure the quality of generated output?
We build a task-level evaluation harness from your real cases that scores generations on accuracy, faithfulness to source, tone, and safety. It runs before launch and on every change, so quality is measured rather than assumed and a prompt change can't quietly degrade what your customers read.
Which models do you build on — OpenAI, Claude, or open-weight?
We are model-agnostic and choose per task, defaulting to the most capable Claude models for demanding reasoning and drafting while routing simpler steps to cheaper or open-weight models. We design so you can switch models as the field moves, rather than locking the system to one vendor.
How much does a generative AI development project cost and how long does it take?
A common path is a fixed-price 2-week Discovery Sprint, then an 8–12 week build to a production pilot. Cost depends on the number of use cases, the grounding and integration work, and governance needs; we size it precisely after the Sprint. Before scoping, any quote is a guess.
Can generative AI run in our own cloud or region?
Yes. We deploy to your cloud and keep data inside the region your policy or regulation requires — UAE, Saudi Arabia, UK, US, or India — and build the audit logging and content governance your risk team needs to approve a system that generates on the company's behalf.
Bring the content, code, or document work that eats the most hours. In 45 minutes we'll tell you where generative AI fits, where it doesn't, and what it would take to put it into production.
Book a 45-min scoping call
