Agentic Unconference 2026 - September 19, Chennai
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We connect LLMs and AI agents to your CRM, ERP, data and internal apps, with the access controls and audit trails security teams ask for. Senior engineers with enterprise delivery behind them get your pilot through security review and into daily use.
AI integration services connect large language models, AI agents and machine learning models to the systems a business already uses. Typical targets are CRM, ERP, data warehouses and service desks. The work spans AI integration consulting, API and tool design, access controls, security review, evaluation and monitoring in production.
✓ Your AI pilot works in a demo but not in production. The model answers well on a laptop, then stalls at security review, single sign-on or data access. Integration work is what gets it through those gates.
✓ One task spans three systems. Someone reads an email, checks the CRM, keys data into the ERP and updates a ticket. AI that reads and writes across those systems removes the copying itself.
✓ Documents drive a slow, manual process. Invoices, claims, contracts and prior authorization forms are strong fits. LLMs extract the fields well, and a rule engine checks them before anything reaches the ERP.
✓ You want to switch models without rewriting apps. Teams that call one provider from dozens of places get stuck when prices or models change. A gateway layer lets you move between GPT, Claude and Gemini with a config change.
> Your SaaS vendor already ships the AI you need. If the job lives entirely inside Salesforce, ServiceNow or Microsoft 365, try Agentforce, Now Assist or Microsoft 365 Copilot first. Custom integration pays off when the work crosses systems or the vendor's AI can't see the data it needs.
> The process is fixed and rule-based. If every step is known in advance, a workflow tool is cheaper, faster and easier to audit than an LLM. Use n8n workflow automation for those steps and save AI for the ones that need judgment.
> Your data isn't ready yet. If customer records conflict across systems or key documents aren't digitized, AI will produce confident errors at scale. An AI readiness assessment shows what to fix first.
We add LLM features to the products and tools you already run: drafting, summaries, search and document extraction. Each feature calls models through your own cloud account and returns structured output that your code checks before a user sees it.
We connect AI agents to CRM, ERP and service desk systems as governed tools, through APIs or MCP servers. Each tool gets scoped permissions, allowed actions and a log entry. Larger multi-agent setups run through our agentic AI framework integration practice.
Many teams hard-coded one provider into dozens of services, or still run on APIs their provider is retiring. We move those calls behind an AI gateway, port them to current APIs and re-test outputs, so the next model swap is a config change.
Models, vector stores and agents run inside your AWS, Azure or Google Cloud account. Traffic stays on private endpoints, behind your existing single sign-on. Our cybersecurity and compliance team reviews data flows, masking and retention before the AI touches production data.
A test set of real inputs and approved outputs runs in CI on every prompt, model or data change. The build fails when quality drops. After launch, our DataOps and MLOps team watches accuracy, latency and cost per task, and handles model upgrades.
Older ERP, mainframe and on-prem systems rarely have clean APIs. We wrap them in a thin, documented API layer so AI can read and write through supported paths. The same layer carries into later AI-driven legacy modernization.
80%
Reduction in Manual Reconciliation Effort 2X Faster Payment Processing
Wired LLM functions on Amazon Bedrock into a Flask, MongoDB and PostgreSQL platform that extracts invoice fields and matches them against purchase orders and goods receipt notes.
3X
Accelerated prior authorization processing by 3X Reduced manual effort and errors 70%
Combined OCR and large language models with a configurable rule engine, and moved a monolithic prior authorization system to microservices with automated testing and reporting.
24%
reduction in manual support responses from automated bot integration
Rebuilt the web platform for a SaaS provider that simplifies compliance for fintechs and banks, and integrated an AI compliance chatbot to take routine support questions off staff.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Every AI integration has a build cost and a running cost. The build is mostly engineering time for connectors, access controls and testing. Running cost is model usage, hosting and monitoring. That cost scales with volume, so a feature used thousands of times a day needs a cost model before launch. A scoped pilot on one workflow gives you both numbers, including cost per completed task, before you commit to a rollout.
Enterprise systems should call models through an API, not the ChatGPT app, because APIs give you access control, logging and structured output. The OpenAI API gets new OpenAI models first. Azure OpenAI runs OpenAI models under your Azure contract, network rules and region. Amazon Bedrock suits AWS teams that want Claude, Llama and other models in one place. If you built on the Assistants API, OpenAI sunset it on August 26, 2026, so those integrations need moving to the Responses API.
MCP is an open standard, introduced by Anthropic in November 2024, that gives AI models one way to find and call tools and data. You need it when several AI apps or agents must reach the same systems. One MCP server per system then replaces many one-off connectors. For a single feature calling one API, a direct integration is simpler. The security work is the same either way: scoped credentials, allowed actions and logs.
AI integration consulting decides what to connect and in what order, and AI integration services build and run those connections. Consulting usually ends with a short list of use cases, a target design and the data and security gaps to close. Integration work turns that plan into working connectors, tests and live monitoring. Keep both with one team, and you never end up with a strategy deck no one can build.
Timeline depends more on system access than on model work. Getting API keys, security sign-off and test data for each system often takes longer than writing the code. One workflow touching one or two systems is pilot-sized. A rollout across business units, with single sign-on, audit and change management, is a multi-month program. Name a security reviewer and a system owner in week one. That saves more time than any tool choice.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
