AI that takes a manual step out of a real workflow.
Inople builds AI agents, MCP servers, document intelligence and GenAI features for companies that already have a working business process. We start from the step that costs your team the most time, prove the model can do it reliably, and wire it into the systems you already run. Senior engineers review every release.
What kind of AI agents do you build?
Inople builds AI agents that do multi-step work, such as researching a prospect, checking the facts against a second source and drafting the next action. We build them on the Model Context Protocol (MCP), so each agent reaches your CRM, database or internal API through a defined, auditable server instead of ad hoc prompts.
Our own platform, TargetGrid, is the clearest proof. It runs autonomous sales research and outreach agents on MCP, and it was selected as one of 101 startups for the Government of Karnataka's Elevate 2024 grant. The same patterns carry over to client work: tool access with clear permissions, logs of every step, and a human approval point wherever a mistake would be expensive.
- MCP servers
- A typed, permissioned bridge between a model and your systems, so agents can read and write data without direct database access.
- Multi-agent workflows
- Separate agents for research, verification and drafting, with handoffs you can inspect.
- Human checkpoints
- Approval steps before anything is sent, paid or changed in a system of record.
Can AI read our documents reliably?
Document intelligence turns scanned or photographed documents into structured data your systems can act on. Inople has built OCR and extraction for identity documents, KYC packs, receipts, invoices and construction tenders. Reliability comes from validation rules, confidence thresholds and a review queue for the cases the model is unsure about, not from the model alone.
Work our team has led includes a branch onboarding system that read identity documents during a loan application, a mobile receipt scanner that extracts line items, tax and totals, and a tender parser that pulls compliance requirements out of long infrastructure tender documents and pre-fills the bid.
How do you add GenAI to a product we already run?
Adding GenAI to an existing product means fitting a model into data, permissions and screens that already exist. Inople has added transaction categorization and personal finance insights to live banking apps, and question answering over large document sets using retrieval-augmented generation (RAG). The model only sees data the user is allowed to see.
- Transaction categorization
- Classifying and labeling transactions inside retail and corporate banking apps.
- RAG over large documents
- Answers grounded in your own documents, with the source passage shown alongside.
- Assistants inside workflows
- Drafting, summarizing and lookup features placed where staff already work.
When is AI the wrong answer?
AI is the wrong answer when a rule, a form or a database query would do the job. If the input is structured and the logic is fixed, a model adds cost, latency and a new way to fail. Inople will tell you that in the first conversation, and we would rather build the plain version that works.
| Situation | Better choice |
|---|---|
| Fixed business rules, structured input | Plain code or a rules engine |
| Data already in a clean form | A better form or validation, not extraction |
| Messy documents, free text, research across sources | AI, with validation and human review |
| A decision that must be explained to a regulator | Rules first, AI only as an assistant |
How does Inople use AI in its own delivery?
Inople uses AI to draft specifications, write and review code, generate tests and keep documentation current. It shortens delivery cycles, but it does not replace judgment. A senior engineer reads and signs off on everything before it reaches a client, so the speed never comes out of quality or security.
Frequently Asked Questions
How long does it take to build an AI agent?
It depends on how many systems the agent has to reach and how much review a wrong action needs. We scope it after a first conversation and agree the timeline before work starts, rather than quoting a number blind.
What is an MCP server?
An MCP server exposes your data and tools to an AI model through the Model Context Protocol. It defines exactly what the agent can read or change, which keeps access controlled and every action logged.
Do you only do AI?
No. Most of our work is product engineering for banking, EV charging and industrial companies. AI is one tool we use where it removes real manual work.
Which models do you work with?
We pick the model per task based on accuracy, cost and where your data is allowed to go. Some work suits hosted models, and some needs a model that runs inside your own cloud.
Can you work with our in-house team?
Yes. We often build alongside an internal team, either owning one part of the system or pairing with your engineers so they can run it after we hand over.
How do you keep AI outputs accurate?
With validation rules, confidence thresholds, test sets built from your real data and human review on the cases the model is unsure about. We measure accuracy before launch and keep measuring after.
Tell Us What You’re Building
A first call is free. You talk to the people who would lead the work, and you leave with a clearer plan whether or not we work together.