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AI Agent Development

01 / 10AI Engineering

Diagram of one agent run: a customer asks for a refund; the AI agent reasons and plans, uses order lookup, policy check, refunds, email and helpdesk tools, which call the orders, refunds, messages and tickets APIs, which act on commerce, payments, helpdesk, CRM and data warehouse systems.

AI that doesn’t just talk. AI that gets things done.

We build production-grade agents that reason over context, choose the right tools, call your APIs and move work through real systems, from the first request to the final confirmation.

  • 01Reasons

    Breaks a request into a plan, and adapts when the plan meets reality.

  • 02Acts

    Calls tools and APIs with scoped permissions, not copy-paste suggestions.

  • 03Delivers

    Closes the loop inside your systems, with a trace you can audit.

What we build

Six agents, each with one job.

Every agent is scoped to a single outcome, wired into your stack and measured against the work it takes off your team.

  1. Customer Support Agents

    Resolve tickets end to end: look up orders, apply refund policy, update the helpdesk and hand off to a person when it matters.

  2. Sales Agents

    Qualify inbound leads, enrich accounts, draft outreach that sounds like your team and book meetings straight into calendars.

  3. Operations Agents

    Reconcile records, chase approvals, watch for exceptions and keep back-office processes moving without manual follow-up.

  4. Internal Knowledge Agents

    Answer employee questions from docs, wikis and past tickets with citations, respecting who is allowed to see what.

  5. Research Agents

    Gather sources, compare findings and deliver structured briefs your team can act on and verify line by line.

  6. Workflow Automation Agents

    Orchestrate multi-step processes across tools, with retries, approvals and a complete audit trail of every action.

How an agent works

Six moves between a request and a result.

  1. Reads the request, the history and the account to work out the outcome that’s actually wanted.

  2. Breaks the job into ordered steps and decides which ones need tools, data or approval.

  3. Calls the right functions with typed, validated arguments, never guessing at what a tool returns.

  4. Writes to your systems through scoped APIs, with idempotent actions and full logging.

  5. Checks the result against policy, limits and the original goal before anything is confirmed.

  6. Replies in your brand voice with what was done, or hands over to a person with full context.

Example run · refund requeststep 1/6 · understand
  1. intentrefund_request · order=#4821 · reason=damaged · sentiment=frustrated
  2. plan1 verify order → 2 check policy → 3 issue refund → 4 notify + close ticket
  3. toolorders.lookup(4821) → delivered 3 days ago · $48.00policy.check(“damaged”, 3d) → eligible · auto-approve ≤ $100
  4. apiPOST /v1/refunds → 201 created · re_19Hk2PATCH /v1/tickets/913 → status=resolved
  5. evalamount within limit ✓ · policy match ✓ · no PII in reply ✓
  6. reply“Sorry about that. Your $48.00 refund is on its way and should reach you in 3–5 days.”

Technical capabilities

Built for production, not for demos.

  • Agents pick the right function and pass typed arguments that are validated before anything runs.
  • Secure connectors to your REST, GraphQL and internal services, each with scoped credentials.
  • Short-term task state and long-term context, stored deliberately and forgotten on request.
  • Plans that branch, loop and resume, surviving timeouts and partial failures.
  • Clear escalation paths with full context, so people pick up exactly where the agent stopped.
  • Policy checks, spend limits, PII filters and allow-lists that hold up under pressure.
  • Every decision, tool call and cost traced end to end, searchable and replayable.
  • Scenario suites and regression tests that score behaviour before and after every change.
  • Model, tool and provider fallbacks with retries, so one outage never becomes your outage.

Process

From first workshop to a live agent.

Short, measurable stages. You see working software early, and every release is scored before it reaches a customer.

  1. 01

    Discover

    Map the workflow, the data, the systems it touches and the outcomes worth automating.

    Opportunity map
  2. 02

    Architect

    Design tools, permissions, memory, guardrails and where people stay in the loop.

    System blueprint
  3. 03

    Build

    Implement integrations and behaviour in short iterations against real scenarios.

    Working agent
  4. 04

    Evaluate

    Score accuracy, safety, cost and latency with test suites and red-teaming.

    Evaluation report
  5. 05

    Deploy

    Ship behind flags with monitoring, audit logs and a clear rollback path.

    Production release
  6. 06

    Optimize

    Tune prompts, tools and models from live traces to raise quality and lower cost.

    Ongoing improvement

From intent to execution.

  1. 01IntentWhat the person actually needs
  2. 02Reasoning & toolsMany possible paths, one chosen
  3. 03ExecutionReal changes in real systems

Next step

Have a workflow worth automating?

Tell us about the process that eats your team’s week. We’ll show you what an agent could realistically take off their plate.

Build an AI Agent

What's our client are say !

Excellent Service

Synapse rebuilt our pet-care store from the ground up. Launch was on time, conversions are up and the team still answers within the hour.

Founder, CYBE Pet Care

A True Partner

They challenged our brief in the best way. The new identity and site feel like us — only sharper. Our campaign traffic doubled in a month.

Marketing Lead, Darkside Apparel

Fast & Thoughtful

From wireframes to App Store in twelve weeks. Every screen was tested with real travellers, and it shows in our reviews.

Product Owner, Book Now Travel