Skip to content
Let's talk

Agent · live queue

TicketRefund for order 4471

CheckedPolicy · order history

ActionRefund issued

Service

AI Agents

Agents that reason, use your tools and finish the job — grounded in your data, with a human one step away.

Discuss your project

The problem

Skilled people spend their days answering the same questions, chasing the same approvals and moving data between systems.

What it is

An AI agent is software that can decide and act, not just reply. It reads your knowledge, makes a plan, and takes real actions: querying databases, triggering workflows, drafting emails, updating your CRM.

How it works

We give each agent a clear role, a set of tools connected to your software, and secure access to your company knowledge. When an event arrives, the agent reasons through the steps, calls the right tools in order, and checks its own work before acting.

How it runs

  1. Request arrives
  2. Context gathered
  3. Action taken
  4. Handed to a person if needed

  • Knowledge retrieval

    Answers grounded in your own documents, tickets and policies.

  • Real actions

    Agents call your internal APIs to read and update systems.

  • Agent teams

    Specialised agents hand work to each other without friction.

  • Human escalation

    When confidence drops, the case goes to a person with full context.

Delivery

From first call to live system in four weeks.

  1. Week 1

    Knowledge

    Index your procedures, past tickets and documentation securely.

  2. Week 2

    Tools

    Connect the agent to your CRM, ERP and APIs with strict permissions.

  3. Week 3

    Shadow mode

    The agent runs alongside your team to prove its accuracy.

  4. Week 4

    Live

    The agent takes live work, with monitored guardrails.

Outcomes

What it changes.

Tickets resolved automatically
70%
Average handle time
< 2 min
Availability
24/7

Where it works

  • IT support

    Agents resolve access issues, reset credentials and route hardware requests to the right team.

  • Sales engineering

    Reps get documentation-backed answers to technical questions while still on the call.

  • Invoice matching

    Agents read vendor invoices, match them against purchase orders and queue them for approval.

Typical stack

  • LangChain
  • OpenAI & Anthropic models
  • Pinecone
  • Redis
  • Next.js

Security

Your data is never used to train public models. We use zero-retention agreements with model providers and remove personal data before any request leaves your environment.

Related work

In production.

FAQ

Questions, answered.

  • We use zero-retention model contracts. Your data never trains public models, and personal data is removed before processing.

  • We ground every answer in your data and add deterministic guardrails. Below the confidence threshold, the agent hands over to a person.

  • Monitoring detects schema changes and alerts our engineers immediately, so nothing fails silently.