Custom AI Agents & Automation
A custom AI agent is software that uses a large language model to complete multi-step tasks using your data and tools.
We design and ship production AI agents — chat, voice, and workflow — built around your data, tools, and customers. From LLM integration to RAG pipelines and evaluations, we take AI work from prototype to something you can put in front of users.
Who this is for
- Teams with a support, sales, or operations queue where the same multi-step task repeats every day
- Product teams adding AI features to an existing application rather than starting a new one
- Companies that have a working prototype and need it made reliable, evaluated, and monitored
- Teams with useful internal documentation that nobody can find when they need it
When it's not a fit
- The task is identical every time. A deterministic script is cheaper, faster, and easier to debug than a model in the loop.
- There is no documentation or data to ground the agent in. Fix that first; an agent cannot retrieve what does not exist.
- Nobody can say what a correct answer looks like. Without that there is nothing to evaluate against and no way to know if it works.
- The goal is a demo for a board meeting. We build systems meant to stay in production.
What We Deliver
Conversational Agents
Domain-tuned chat and voice assistants for support, sales, and internal teams — with memory, tool calling, and human handoff.
Workflow Automation Agents
Multi-step agents that work across your stack — CRM, ticketing, docs, and APIs — to take over repetitive operational work.
RAG & Knowledge Search
Retrieval-augmented systems that ground answers in your private documents, with evaluations and observability built in.
AI Features In Your Product
Copilots, summarisation, classification, and generation shipped inside your existing application, end to end.
Fine-Tuning & Evaluations
Fine-tuning, dataset curation, and evaluation suites so behaviour is measured on every change rather than guessed at.
AI Strategy & Roadmaps
Advisory work to find the use cases worth doing, scope a pilot, and define the path from prototype to production.
Common Use Cases
- 24/7 AI customer support and onboarding agents
- Sales and lead-qualification copilots
- Internal knowledge search across docs, wikis, and tickets
- Document and contract intelligence for operations teams
- Voice agents for inbound and outbound calls
- Workflow automation across CRM, billing, and project tools
- Domain assistants for healthcare, fintech, education, and Web3
Tech Stack
Outcomes you can expect
- Faster responses and round-the-clock coverage on the queue you pointed it at
- Less time spent on repetitive multi-step work
- Answers grounded in your documents, with the source passages attached
- Behaviour you can measure on every change, rather than after a complaint
What you receive
Everything below is handed over to you. Code and infrastructure live in your accounts, not ours.
- The agent running in your own cloud accounts, with infrastructure as code
- Source code, prompts, and tool definitions in your repositories
- An evaluation suite built from real examples, runnable in CI
- Retrieval pipeline and index, with the ingestion job that keeps it current
- Logging and monitoring for every tool call, with inputs and results
- A written handover covering the architecture, failure modes, and how to extend it
Engagement models
Fixed-scope project
A defined deliverable at a fixed price, scoped up front. Best when you know what you need built and want a firm schedule and budget.
Best when: You know what you need built and want a firm budget and date.
Retainer
Ongoing delivery, maintenance, or advisory billed monthly. Best for continuous improvement after launch, or when priorities shift faster than a fixed scope allows.
Best when: The work is continuous and priorities shift faster than a fixed scope allows.
Extended team
Our engineers working inside your team, your process, and your repositories. Best when you have the roadmap but not the capacity or the specific skills.
Best when: You have the roadmap but not the capacity or a specific skill.
Frequently asked questions about Custom AI Agents & Automation
A custom AI agent is software that uses a large language model to complete multi-step tasks using your data and tools. It can retrieve documents, call your APIs, decide what to do next, and hand off to a person when it cannot finish the job itself.
A chatbot answers questions from a script or a single model call. An agent plans across several steps, uses tools such as your CRM or ticketing system, and takes action — creating records, sending messages, or escalating to a human. We build both, depending on whether you need actions or only answers.
We work with OpenAI, Anthropic Claude, Gemini, and Llama, and with Azure OpenAI where the model needs to sit inside your own cloud. We are not tied to one provider: we choose per use case on cost, latency, and output quality, and keep the integration swappable.
With retrieval-augmented generation. Your documents are indexed in a vector store such as Pinecone, Weaviate, or pgvector, and the agent retrieves the relevant passages before answering, so responses come from your material rather than the model's general knowledge. Evaluations and observability then catch drift.
You do. Code, prompts, evaluation datasets, and infrastructure definitions are delivered into your repositories and cloud accounts. There is no licence you have to keep paying for to keep using what we built, and we do not train shared models on your data. Ownership is set in the engagement agreement.
Yes. Agents connect to the systems you already run — CRM, ticketing, billing, documentation, and internal APIs — through tool calling. Most of the engineering effort goes into those integrations and into handling their failure cases properly, rather than into the model itself.
We build an evaluation set from real examples and run it on every change, so you can watch accuracy move instead of guessing. Guardrails constrain what the agent is allowed to do, and production monitoring surfaces failures and unexpected inputs so they can be fixed.
Discovery. We agree the goal, review whatever exists already, and produce a written scope: what will be built, in what order, what you will receive, and what we need from you. It ends with a plan you can approve, amend, or take elsewhere.
The outcome you want and how you will judge it, access to any existing code, designs, hosting, and analytics, one person who can make decisions, and any hard constraints — a fixed launch date, a required stack, compliance obligations. Missing pieces we can work around; an unnamed decision-maker we cannot.
Both. Our published work spans early-stage products and established companies, and our smallest engagements are single fixed-scope projects. What matters is that someone on your side can make decisions and that the problem is defined well enough to scope, not company size.
Yes, as a retainer covering monitoring, security patching, dependency upgrades, performance work, and a monthly allowance for fixes and small changes. You get a report each month showing what was done. It is a separate engagement from the build, so upkeep does not compete with feature work.
Engineering and design work is delivered remotely, so location is not a constraint for those. Our in-person work — hackathons, builder houses, developer meetups, and campus programmes — runs in Delhi, Bangalore, Hyderabad, and Mumbai, plus university campuses across the country.
You get a written summary of what we understood, a proposed scope broken into phases, what each phase delivers, and the engagement model we would recommend. If we think you should buy something off the shelf or not build yet, the summary says that instead.