OpenAI & multi-model APIs
Integration with OpenAI, Azure OpenAI, Anthropic, Google Gemini and AWS Bedrock through one abstraction layer.
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Enterprise AI Integration
Add AI features to the software you already run: summarise records, draft replies, extract data, classify tickets or answer questions. We integrate large language models behind a secure API layer that your applications can call reliably.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
Production LLM features need more than an API key. We add prompt versioning, response validation, caching, rate limiting, model fallbacks, PII redaction and cost tracking, so AI features behave predictably inside your CRM, ERP, helpdesk or product.
Discuss your requirementSaaS companies adding AI features for their customers
Enterprises connecting AI to ERP, CRM or document systems
Teams whose quick AI prototype now needs to be production-ready
Businesses that must keep data in India or in their own cloud
Capabilities
Integration with OpenAI, Azure OpenAI, Anthropic, Google Gemini and AWS Bedrock through one abstraction layer.
Your own internal AI endpoints (summarise, classify, extract, draft) that every application can reuse.
JSON schema validation and function calling so AI responses drop cleanly into forms, databases and workflows.
PII masking, tenant isolation, zero-retention settings and audit logs for every request.
Caching, batching, model routing and token budgets that keep response times and bills in check.
Llama, Mistral or Qwen deployed in your cloud when data cannot leave your environment.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Inputs, outputs, users and quality criteria are specified.
Candidate models are compared on your sample data.
Secure endpoints with validation, logging and fallbacks.
Features are wired into your UI and workflows behind feature flags.
Quality, latency and cost dashboards with alerts.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
Business API offerings from the major providers do not use API data for training by default, and we configure zero or minimal retention where available. For highly sensitive data we can host an open-source model in your own cloud.
Yes. We connect through the system APIs or a middleware layer, adding features such as drafted replies, record summaries, data extraction and smart search without replacing the core system.
Our integration layer can fall back to a secondary model or provider, queue non-urgent requests and degrade gracefully, so your application keeps working.
We route simple tasks to smaller models, cache repeated requests, trim prompts and set per-user or per-feature budgets with alerts, and you see the cost of each feature on a dashboard.
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