LLM and AI agents
LLM and AI agent development, built for production.
We build assistants and autonomous agents grounded in your data and wired into your tools, then evaluate them like any other critical system. Not demos: agents that act inside policy, explain themselves and hand off when they should.
Start a projectWhat makes an agent production-ready
A good AI agent is mostly engineering: retrieval that finds the right facts, tools with tight permissions, policies enforced in code, an evaluation suite that catches regressions, and an audit trail for every action. The model is the easy part.
Overview
What LLM and AI agent development involves
Large language models can read, write and reason over text, but on their own they know nothing about your business and cannot act in your systems. LLM application development connects a model to your knowledge, usually through retrieval-augmented generation, so it answers accurately with sources. AI agent development goes further: the model gets tools, such as searching a database, updating a CRM record or creating a ticket, and plans the steps needed to finish a task.
The difference between an impressive demo and a reliable agent is engineering discipline. We define exactly what the agent may do, give each tool the narrowest permissions, enforce business rules outside the model, and measure quality with an evaluation set drawn from your real work. Agents run in shadow mode before they act on their own, and every decision is traceable. The result is an agent your operations, security and compliance teams can sign off.
What we build
LLM and AI agent development services
Each system is designed around a measurable job, then hardened with guardrails and evaluation before it touches real users.
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01
Retrieval-augmented assistants (RAG)
Answers grounded in your documents, tickets, wikis and code, with citations, permission-aware search and freshness controls.
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02
Tool-using AI agents
Agents that read and write to your CRM, ERP, helpdesk and databases through scoped tools, with approvals for anything risky.
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03
Multi-step workflow agents
Agents that plan and execute multi-step tasks such as research, reconciliation or onboarding, with checkpoints a person can review.
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04
Evaluation and red-teaming
Golden datasets, automated graders, regression suites and adversarial testing, run on every change to prompts, models or tools.
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05
Model selection and cost control
OpenAI, Anthropic, Google or open-weight models chosen per task, with routing, caching and limits that keep unit costs predictable.
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06
LLMOps and monitoring
Tracing, quality dashboards, drift alerts and incident runbooks, so the agent keeps working after launch.
Process
How we build an AI agent
Fixed fee per phase, a measured exit gate at the end of each, and working software from the first weeks. See every playbook week by week.
- 01
Map the job
Walk the workflow with the people who do it. Define tools, permissions, policies and what success means in numbers.
Weeks 1 to 2
- 02
Build and evaluate
Retrieval, tools and the agent loop, plus an evaluation set built from your real cases and edge cases.
Weeks 3 to 5
- 03
Shadow mode
The agent drafts, your team decides. We measure agreement, time saved and cost on live work.
Weeks 6 to 7
- 04
Launch and run
Staged rollout behind a kill switch, with dashboards, alerts and a rehearsed runbook.
Week 8 onward
Use cases
Where LLM agents pay back first
Internal knowledge copilots
One assistant over policies, SOPs, tickets and code, answering with sources and respecting permissions.
Customer support agents
Resolve tickets end to end through your systems and escalate edge cases with a drafted reply. See AI chatbot development.
Sales and proposal agents
Research accounts, draft proposals from your past wins and keep the CRM up to date.
Finance and operations agents
Reconcile statements, chase missing documents and prepare reports for review.
Engineering copilots
Agents for code migration, test generation and incident triage inside your repositories.
Compliance and risk review
Check documents and decisions against policy, with every finding linked to its source.
- Evaluation first: we build the test set before the agent, so quality is measured from day one.
- Guardrails in code, not prompts: refunds, approvals and data access are enforced by the system.
- Model-agnostic: we pick the model per task and can switch as prices and quality change.
- Full audit trail of every tool call and decision, ready for security and compliance review.
- Senior engineers who build for production, not prompt experiments.
What we build with
Chosen for your problem, never for a partnership.
AI and data
- OpenAI
- Anthropic
- Google Gemini
- Llama and Mistral
- LangGraph
- pgvector
- Databricks
- Snowflake
Product
- TypeScript
- React
- Next.js
- React Native
- Flutter
- Python
- Go
- Node.js
- Postgres
Platform
- AWS
- Google Cloud
- Azure
- Kubernetes
- Terraform
- GitHub Actions
Questions
Frequently asked questions
Still unsure? Ask us directly, a senior engineer replies within one business day.
What is an AI agent, and how is it different from an LLM application?
An LLM application answers or generates content, for example a knowledge assistant. An AI agent also takes actions through tools, such as updating a CRM or issuing a refund, and plans multiple steps to finish a task. Our guide what is an AI agent explains it for business leaders.
Should we use RAG or fine-tuning?
Most business assistants should start with retrieval-augmented generation (RAG), because it keeps answers grounded in current documents and is cheaper to update. Fine-tuning helps with format, tone or narrow tasks. Our guide RAG vs fine-tuning explains how to choose.
Which LLMs do you work with?
OpenAI, Anthropic, Google Gemini and open-weight models such as Llama and Mistral, hosted by the provider or in your own cloud. We choose per task based on quality, latency, cost and data residency.
How do you stop an AI agent from making mistakes?
Scoped tools with least-privilege permissions, policy checks enforced in code, confidence thresholds that route uncertain cases to people, and an evaluation suite that runs on every change.
Can the agent run on our private data securely?
Yes. We deploy in your cloud, respect existing permissions in retrieval, keep data within your chosen region and never use your data to train models for anyone else.
How long does it take to build an AI agent?
Our plan for a focused agent is shadow mode in about six weeks and live production in about eight, after a two to four week proof of value.
What does an AI agent need access to?
Only what its job requires. We give each tool least-privilege access, add approval steps for sensitive actions and log every call, so access can be reviewed and revoked like any other system user.
Are you an AI agent development company that works with clients across India?
Yes. We build AI agents for companies across India and for international teams, from our base in Ahmedabad. Agents commonly combine a knowledge base, a CRM or ERP, a ticketing system and email, and each integration is a scoped tool with its own tests and monitoring.