Generative AI · United States
Banao develops generative AI for US mid-market and enterprise teams: LLM pipelines, RAG systems, and domain-adapted models built to SOC 2-aligned controls, US data residency, and the output governance that US legal and compliance teams require before any AI acts on customer or operational data.
We deliver from a California presence backed by a ~300-engineer bench in India. For US buyers who need a vendor accessible on the West Coast for scoping calls and able to start a build in weeks — not the months a local hire would take — that combination is why teams bring us in.
Banao— Vikaas, our own generative AI demand engine, runs on a fine-tuned model supporting Banao's US and California pipeline in production daily.
Each capability is scoped to the compliance, data-residency, and output-governance expectations US procurement and risk teams now apply to any AI that acts on real data.
Retrieval-augmented generation built over your document corpus — policies, contracts, product documentation, internal knowledge — so the model retrieves from authorised sources rather than interpolating from pre-training, and every output can be traced to a specific retrieved passage.
All model inference, retrieval operations, logs, and generated outputs kept in US-based infrastructure, with access controls, encryption, and audit logging built to SOC 2 Type II practices — a design decision made in the first sprint, not retrofitted when your security team asks.
We fine-tune language models on your own labeled examples using LoRA or QLoRA, so the model produces output in your vocabulary, your format, and with the precision your US operational context requires — not a general approximation of it.
Output filters and allow-lists enforced in code rather than left to model discretion, so generated content meets your legal, HR, and brand standards before it reaches a user or acts on a record — a US legal team requirement on any AI in a customer-facing or regulated workflow.
Data flows, consent handling, and audit trails designed around California Consumer Privacy Act requirements, HIPAA where healthcare data is in scope, and the financial services guidance that applies to your sector — not a generic privacy checklist.
A sign-off layer on consequential AI outputs — contract language, financial summaries, customer communications — with the model's retrieved sources and reasoning visible to the reviewer, meeting the explainability bar US compliance teams now set for AI that acts on records.
A task-level evaluation suite built from your real US prompt distribution, run before launch and after every change, so a model update or prompt revision cannot silently degrade accuracy on the outputs that matter to your operation.
For US enterprises where data cannot leave a controlled environment — healthcare systems, financial institutions, government contractors — we build and deploy to private cloud or on-premise infrastructure, with model quantisation and optimisation for your target hardware.
Two pressures converged on the US mid-market in 2024–2025. First, a structural labor cost problem: back-office and knowledge-work headcount grew with revenue for a decade, and the economics of that model no longer hold at the volume mid-market companies need to operate. Second, the first round of generative AI pilots — mostly wrappers around public APIs — produced demos that looked useful but could not survive IT security review, privacy counsel sign-off, or the basic audit question of what the model did with customer data and where it went.
The result is a specific buying moment: US companies that already believe generative AI can help and are now asking which vendor can build something that passes the compliance gate, keeps data in US infrastructure, and has an accuracy story that goes beyond the demo. That is not a pilot — it is a production build, and it requires a different approach than a quick API wrapper.
Banao's California presence means we can be in that procurement conversation this week. The ~300-engineer delivery bench means the build starts without a hiring cycle.
SOC 2 alignment, US data residency, CCPA handling, and a human-readable audit trail are now standard asks from US enterprise IT security and legal before any AI that reads or generates from customer or operational data goes to production. We design for those requirements from the first sprint.
US service and knowledge-work businesses face headcount costs that have risen sharply since 2020, while revenue growth demands more throughput from the same teams. Generative AI handles document analysis, drafting, triage, and reporting at a unit cost that changes the economics of those workflows — specifically, not generically.
US enterprise risk and compliance teams now reference the NIST AI Risk Management Framework when assessing AI vendors. Sector rules — HIPAA for healthcare, FERPA for education, financial services guidance from OCC and CFPB — add specific requirements on top. We build the controls that map to those frameworks, so documentation exists because the controls exist.
The combination US mid-market buyers often cannot find: a vendor in California for scoping calls and architecture reviews, with a ~300-engineer bench that starts the build in weeks. No months assembling a local team, no time-zone mismatch during an active engagement.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. The deployments are live.
Banao — Vikaas
Vikaas plans, drafts, and sequences Banao's own outreach — including US and California accounts — using a fine-tuned language model with retrieval grounding and output logging. We run our own US revenue pipeline on it before we offer the pattern to a US client.
US SaaS business (anonymized)
A generative AI pipeline reads incoming US contract documents, extracts the clause and field data the client's legal and operations teams need, and routes edge cases to a human reviewer. All processing stays in US-based infrastructure; the retrieval index is built on the client's own precedent library.
Banao built and operates its own generative AI before offering it to US clients. Vikaas, our demand-generation system, runs on a fine-tuned language model — it processes lead data, drafts outreach, and handles Banao's US and California pipeline on a daily basis. InterviewGod uses generative AI to assess engineering applicants against role-specific criteria, running on Banao's own hiring process every week.
For a US buyer deciding whether to trust a vendor with workflows that touch customer data or operational records, the relevant question is whether the vendor depends on the same technology in their own business. We do — with evaluation before deployment, audit logging from day one, and a team that has met the operational discipline generative AI in production requires.
US enterprises have often been through an AI pilot already. We would rather tell you what will not work than sell a build that stalls at your compliance gate:
US teams have often already run a generative AI pilot on a public API. We start by finding which of your use cases can clear the compliance and accuracy bar that production requires.
We take your candidate use case, assess it against your US compliance requirements, test candidate approaches on your actual prompts and data, and hand back a scoped architecture, an evaluation plan, and ROI maths — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
We develop the pipeline — RAG, fine-tuning, guardrails, approval gates, governance logging — with US data residency, SOC 2 alignment, and an evaluation harness as deliverables, not afterthoughts added before go-live.
We deploy with a cost-per-query dashboard and a live eval signal, re-tune as your workload evolves, and extend the system to adjacent use cases as measured performance earns it.
Can you build generative AI for a company in the USA?
Yes. Banao has a California presence for client-facing engagement and a ~300-engineer delivery bench in India. We build generative AI for US mid-market and enterprise teams — scoped from California when it helps, with the build starting in weeks rather than the months a local hire would take.
Does generative AI built for US clients keep data in the United States?
Yes. We deploy to US-based infrastructure — AWS US regions, Azure US, GCP US, or your own private cloud — with US data residency as a hard architectural constraint. All model inference, retrieval operations, logs, and generated outputs stay in US boundaries, designed in from the first sprint rather than retrofitted when your legal team asks.
How do you handle SOC 2 requirements for generative AI in the USA?
We build access controls, audit logging, encryption, and incident response procedures into the generative AI architecture from the start — not bolted on after the build. When your US enterprise security team runs their review, the controls are present because we designed them in at the architecture stage.
Can you build generative AI that meets CCPA or HIPAA requirements?
Yes. For California Consumer Privacy Act compliance we design data flows, consent handling, and deletion capabilities into the pipeline from the start. For HIPAA-governed healthcare workflows we apply the additional controls — encryption, audit trails, BAA-compatible architecture — that covered-entity status requires. We assess the specific regulatory regime at the Discovery Sprint stage.
What generative AI company in the USA should we work with?
The right choice depends on whether the vendor can meet your compliance requirements, keep data in US infrastructure, and produce measurable accuracy on your actual workload — not just a demo. We earn those answers with a fixed-price Discovery Sprint rather than asking you to trust a proposal built from a brief.
How does generative AI help US mid-market companies specifically?
US mid-market businesses face knowledge-work throughput demands that headcount growth alone cannot meet at current labor costs. Generative AI handles document analysis, drafting, triage, and structured data extraction at a cost that changes the unit economics of those workflows. We build the ROI model in the Discovery Sprint so the numbers are clear before you commit to a build.
Which US industries do you build generative AI for?
We have built for SaaS and technology companies, financial services operations, healthcare-adjacent workflows with HIPAA architecture, and professional services firms — any US operation where the task involves reading, analyzing, or generating from documents, data, and internal knowledge at scale.
How do we start without committing to a full generative AI build?
The AI Discovery Sprint is a fixed-price, two-week engagement. We assess your use case against your US compliance requirements, test candidate approaches on your actual prompts and data, and hand back a scoped architecture, evaluation plan, and ROI model — yours to keep whether or not you continue. If you proceed, the Sprint fee is credited against the build.
Bring the document-heavy, knowledge-intensive workflow that your US team spends the most hours on. In 45 minutes we will tell you whether generative AI is the right build — and what it would take to pass your compliance review.
Book a 45-min scoping call
