Custom AI agents that do real work.

#What an AI agent can take off your plate

An AI agent is software that works towards a goal: it reads the request, finds what it needs, takes the steps you allow, and asks a person when it should. A few common jobs:

• Customer support agent. Answers from your help center and order data, resolves routine requests, and passes the rest to your team with a summary.
• Internal knowledge assistant. Staff ask a question in Slack or Teams and get an answer from your policies, manuals or contracts, with the source attached.
• Sales assistant. Researches a prospect, updates the CRM after a call, drafts the follow-up and books the next meeting.
• Operations agent. Watches a queue, such as orders, claims or applications, handles the standard cases and escalates the exceptions.
• In-product copilot. Helps your own users get things done inside your software.

#Agent, chatbot or automation: which do you need?

These overlap, and the names are used loosely. Here is the practical difference.

If your task is closer to the first row, AI automation is simpler and costs less. We will tell you if that is the case.

#How we build agents you can trust

Anyone can connect a model to a chat window in an afternoon. AI agent development services earn their fee in the parts that make an agent dependable.

• Grounded in your data. The agent answers from your documents and records and shows its sources. It is built to say "I don't know" when the answer is not there.
• Tools with permissions. Each action is a defined tool with its own rules about who can use it and when approval is required.
• Guardrails. Checks on what goes in and what comes out, covering off-topic requests, sensitive data and unsupported claims.
• Evaluation. We collect real examples with the answers you would expect, and run them before every change. You see a score, not a feeling.
• Observability. Every conversation and action is logged, with cost, so problems can be found and fixed quickly.
• A way out. The agent hands over to a person smoothly, with the conversation so far attached.

#How the build runs

• Week 1: design. We define the job, the boundaries, the tools and the handover rules, and gather the evaluation examples.
• Weeks 2 to 3: build. Knowledge base, tools and the agent itself. You can try it from the end of week two.
• Week 4: harden. Guardrails, evaluation runs, logging and the cost dashboard. We tune against the examples until the results are where they need to be.
• Week 5: launch. Rollout to your channel, usually to a small group first, then handover and documentation.

Simpler agents finish in three weeks. You get a demo every week throughout.

#LLM and RAG development for product teams

If you are adding AI to your own product, we can work as an extension of your engineering team. Typical work includes retrieval over customer data with proper tenant isolation, streaming chat interfaces, structured extraction, summarization and evaluation pipelines that run in CI. We work in your repository and your stack, and leave you with code your team can maintain.

Building a new AI product from the ground up? That is MVP development, where the agent is part of a complete product.

#Where to start

If you know the job you want an agent to do, book a call and we will tell you what it involves. If you are still working out where an agent would help most, the AI Opportunity Audit is a one-week way to find out.

#Guides for choosing and scoping an agent

If you are still deciding what an agent should do, these plain-language guides may help.

• AI agents for business: what they do, what they cost and where to start.
• By area: customer service, sales, marketing, finance, e-commerce, real estate and small business.
• How much does an AI agent cost? Buying, building and running costs.
• AI agent vs chatbot: which one you actually need.