AI Integration & Platform Engineering
AI Integrations That Survive Contact With Production
Most AI integrations ship on time and break in production — the demo never saw your real latency, your auth layer, or the edge cases live data surfaces. The model was never the hard part; wiring it into the systems your business actually runs on is. Banao deploys AI into existing enterprise infrastructure — ERPs, CRMs, data lakes, payment rails — with the orchestration, telemetry, and governance that keep it running after launch. We built this on ourselves first: the integration layer connecting InterviewGod, Vikaas, and Vidya across our own 300-person operation is the same platform engineering we deliver to clients.
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What we deliver
The integration layer is where AI projects actually fail
Teams budget for the model and underestimate everything around it — the data contracts, the failure handling, the auth, the monitoring that tells you when inference quietly degrades. That connective tissue is most of the work, and the part that decides whether AI reaches production at all. Banao has built this layer at production scale: payments-grade integration for PhonePe, commerce platforms for Swiggy and Myntra, and enterprise systems for Indian Oil and HCL. We run the same instrumented, governed integration stack across our own operation in India, the UAE, the UK, and the US.
Wire models into the systems that run your business
We embed inference into your ERP, CRM, and internal apps and orchestrate the multi-step flows around it — retries, fallbacks, and human checkpoints — so a model call behaves like production infrastructure, not a fragile demo endpoint.
APIs and serving that hold under real load
Model-serving endpoints, data-access APIs, and microservices built with the rate limits, versioning, and latency budgets production traffic demands — so the integration doesn't fall over the first week it sees real volume.
Pipelines your models can actually trust
Unified data pipelines and lakes engineered for AI workloads — with the lineage, validation, and freshness guarantees that keep training and inference from quietly learning the wrong thing.
AI on top of the systems you can't rip out
We integrate AI with legacy and monolithic systems and stand up hybrid-cloud paths around them — so you add intelligence without a multi-year replatform you can't afford to stall on.
Deployed where your data is allowed to live
Cloud, on-premises, or air-gapped — we deploy inference where your data-residency and regulatory constraints require it, instead of forcing your workload to where the model happens to be convenient.
Telemetry that catches drift before users do
Monitoring, logging, and security controls instrumented across every model and integration — so you see latency creep, accuracy drift, and anomalous calls on a dashboard, not in a customer complaint.
Governance auditors will actually accept
Access controls, privacy guardrails, and audit trails wired into AI workflows and mapped to GDPR, HIPAA, and RBI obligations — generated from live telemetry, not assembled the week before a review.
Connectors for the systems no vendor supports
When the off-the-shelf connector doesn't exist, we build it — bespoke integrations and platform extensions for the proprietary and internal systems that make your environment specifically yours.
How we deliver
How Banao runs an integration engagement
- 01
Discovery & Architecture Planning
We map your existing systems, data sources, auth model, and the business outcomes the integration has to serve, then define the architecture and integration contracts before any code is written. Why this matters: most integrations break on assumptions about systems no one documented. We surface them up front, not in production.
- 02
API & Connector Design
We design the APIs, connectors, and data contracts that move data between your systems and the models in real time, with versioning and rate limits built in. Why this matters: an integration with no contract is a future outage — we define the interface so a change on either side doesn't silently break the pipeline.
- 03
Platform Build-Out & Integration
We build the data platform and orchestration, then embed the models into your live workflows with retries, fallbacks, and human checkpoints where judgment is required. Why this matters: a model that returns an answer is not the same as a workflow that survives a bad answer — we engineer for the second.
- 04
Testing & Validation
We test integrations against real data volumes, failure conditions, and security and compliance requirements before launch — including the edge cases the demo never produced. Why this matters: the latency, auth, and malformed data that surface only at production scale are exactly what we validate against here, not after go-live.
- 05
Deployment & Monitoring
We deploy across cloud or on-premises to match your data-residency rules, then instrument monitoring, logging, and alerting on every model and integration. Why this matters: AI degrades quietly — accuracy drifts, latency creeps. Telemetry is what turns that into an alert instead of a customer complaint.
- 06
Ongoing Optimization & Support
We monitor platform health, tune integrations as load and data shift, and extend connectors as your systems change. Why this matters: integrations rot as the systems around them change. Ongoing ownership keeps the platform running instead of decaying into the next migration project.
Recent work
AI Integration & Platform Work
An enterprise client had AI models proven in isolation but no way to act on them inside daily operations. Banao engineered the integration layer connecting those models to their ERP, CRM, and data lakes, with the orchestration and monitoring to run it in production. Decisioning that lived in notebooks moved into the systems teams actually work in.
Client reviews
What changes once the integration holds
“Banao connected our models to the ERP and CRM where our teams actually work, with the monitoring to keep it running. The integration held under real load instead of breaking the first busy week.”
“They built one data platform for our health analytics — models integrated, access governed, audit trail intact. Compliance approved it because the controls were wired in from the start, not bolted on after.”
FAQ
Frequently asked questions
We've shipped AI integrations before and they broke in production. Why would this be different?
Usually the model wasn't the problem — the integration around it was: latency the demo never saw, an auth edge case, malformed data from a system no one documented. We engineer for those conditions before launch and instrument the integration so degradation shows up as an alert, not an outage. We've broken and fixed our own AI integrations internally since 2017 — that scar tissue is what you're hiring.
Can you integrate AI with our legacy and on-premises systems, not just clean cloud APIs?
Yes — that's most of the work we do. We integrate with legacy monoliths, on-premises systems, and air-gapped environments, building the connectors that don't exist off the shelf. You add AI without a multi-year replatform, and your data stays where your residency rules require.
How do you handle security, data governance, and compliance in the integration?
Access controls, encryption, and audit trails are wired into the integration from day one and mapped to the frameworks you answer to — GDPR, HIPAA, RBI. Reporting is generated from live telemetry, so an audit is a query, not a fire drill. We sign a mutual NDA before any detailed technical discussion.
Who owns the integration code, pipelines, and platform you build?
You do — 100%. The APIs, connectors, pipelines, and platform we build are yours to keep, run, and extend. We don't retain your data or build derivative products from it.
How long does an AI integration project take, and what does it cost?
A focused integration typically runs 4–8 weeks depending on the number of systems, data complexity, and compliance scope; a full platform build-out runs longer. In-house, the same work often stretches to 3–6 months because it competes with everyone's day job. The exact number comes after a short scoping call — book a 45-min call and we'll size it against your systems.
Why not build this with our in-house engineering team?
You can, and some clients do. But integration is pattern work — auth, retries, data contracts, drift monitoring — and the patterns are what compress the timeline. We've accumulated them across eight years of production deployments and run the same stack on our own 300-person operation. Many teams start in-house and bring us in once the integration stalls; we'd rather save you that detour.
What happens after launch — do the integrations keep working as our systems change?
That's where most integrations rot: systems on either side change and the pipeline silently breaks. We offer ongoing ownership — monitoring platform health, tuning as load shifts, and updating connectors as your systems evolve — so the integration keeps running instead of becoming the next migration project.