About Fig AI Systems

Agentic AI systems plan, use tools, and take multi-step actions toward a goal instead of answering one prompt at a time. They are built around a business's own data, rules, and software. Start with what agentic AI is.

There are seven services:

• Custom AI agent development - agents that plan, call tools, and act on real workflows.
• Agentic chat assistant development - conversational agents that complete tasks.
• Multi-agent orchestration and agentic workflow automation - coordination across agents, APIs, documents, and internal software.
• Vector databases, retrieval, and data clustering - the data foundation that grounds agents in your context.
• Closed-loop AI evaluation and testing - harnesses that find failures and feed them back in.
• MCP server development - one server that exposes your tools and data to any assistant, plus submission to Copilot, Claude, and OpenAI.
• Self-learning systems - feedback loops that turn corrections into better behaviour.

The seven overlap on purpose. A retrieval layer with no evaluation loop drifts quietly. The services overview covers how they fit together.

We design each layer explicitly:

• Data foundation. Retrieval, semantic search, and clustering that turn scattered data into usable context.
• Orchestration. Planning and routing that break a workflow into steps and keep state across the run.
• Integration. APIs and workflow handoffs so agents act inside the systems teams already use.
• Evaluation. Closed-loop testing where agents exercise agents, surface failures, and route results back into the build.
• Human review. Gates on high-stakes decisions, so a person stays in control.

Security boundaries and governance are decided at the start. Adding them after an agent writes to production means rebuilding it. The closed-loop evaluation service covers the measurement half.

Build-a-Dress is a live made-to-order fashion business with 2,000+ monthly active users. Shoppers design a dress through an agentic chat assistant with image generation. Orders route to verified manufacturers. See the Build-a-Dress product page.

Report Grader is a domain-specific AI analysis system for education teams. It grades school reports against national and international standards. More than 1,000 token-efficient LLM calls run per report analysis. Human-in-the-loop grading lets inspectors intervene. See the Report Grader product page.

Both product pages go into detail.

Agentic AI fits when:

• the work repeats often enough that small gains compound;
• the workflow needs actions: quoting, routing, grading, updating records;
• a reviewer can say whether an output was right;
• the context an expert needs lives in retrievable documents or systems.

It is the wrong tool when:

• the task is one deterministic rule that ordinary software already handles more cheaply and more reliably;
• nobody can define what a correct output looks like;
• a scripted question-and-answer bot would do the job - see AI agents vs chatbots for that line.

A scoped pilot that proves a workflow is not worth automating is cheaper than a platform build nobody uses.

1. Workflow discovery. We map the current process and find where time and errors accumulate. Success criteria are agreed up front. The output is a recommendation, sometimes not to build.
2. Scoped pilot. One workflow, a fixed scope, and the success criteria set during discovery. It answers one question: does this work on our data?
3. Production. If the pilot holds up, monitoring and evaluation loops are hardened. The system rolls out in phases, with room for more workflows and teams.

The contact page lists what to include. The FAQ answers what comes up before discovery.