We build AI agents that run your operations.

Not chatbots. Not copilots. Autonomous agents that retrieve data, make decisions, call APIs, and execute tasks, inside your actual business systems.

47 agents in productionp95 430ms decision latency0 hallucinations in prod (eval-gated)
underwriter-agent v3.1
running · 4 tools · 2 humans-in-loop
LIVE

An agent is not a chatbot.

It perceives, reasons, acts, and reports, in a loop, without a human in the middle for every decision.

Perceive

Reads emails, APIs, databases, documents

Reason

LLM decides what action to take

Act

Calls tools: APIs, databases, workflows

Verify

Evals check output before it commits

Report

Logs decision, cost, latency, escalations

MetaSys agents are eval-gated. Every decision is checked against test suites before it executes. No hallucination ships to production.

Production agents, not demos.

Three systems we've shipped and operate.

LOGISTICSCase Study

Freight Dispatch Agent

Problem

Manual dispatchers spending 4 hrs/day matching loads to carriers

  • Pulls available loads from load board API
  • Scores 50+ carriers on cost, ETA, and compliance
  • Assigns and confirms dispatch, at $0.018 per decision

310ms

avg decision time

$0.018

cost per dispatch

99.2%

assignment accuracy

US freight operator · Live since Q3 2024

FINTECHCase Study

Underwriting Intelligence Agent

Problem

Risk analysts reviewing 200+ applications/day manually

  • Retrieves policy docs and applicant data in parallel
  • Scores risk via ML model integration
  • Auto-approves tier-2, escalates tier-1 to human

94.3%

decision accuracy

12×

faster than manual

2

humans-in-loop

UAE insurance platform · Live since Q1 2025

CUSTOMER OPSCase Study

Support Resolution Agent

Problem

19,200 support tickets/month, 68% resolvable without humans

  • Classifies and routes tickets automatically
  • Resolves Tier-1 issues via knowledge base + API calls
  • Escalates with full context pre-filled for the agent

68%

auto-resolved

19.2k

hrs saved / mo

4.8★

CSAT maintained

SaaS platform, US · Live since Q2 2024

Every agent we build includes:

Vector memory + retrieval (RAG)

Agent has context about your business

Tool registry

APIs, databases, and services it can call

Eval suite

Test cases run before every production deploy

Human-in-loop escalation

Defined thresholds trigger human review

Observability dashboard

Every decision logged with cost + latency

Feedback loop

Wrong decisions retrain the next version

Guardrails

Hard stops on actions the agent cannot take

Multi-agent orchestration

Agents that spawn and coordinate sub-agents

How we go from problem to production

01

Discovery & Scoping

2 weeks

We map your current workflow, identify the decision points an agent can own, and define what 'good' looks like in measurable terms.

02

Architecture & Eval Design

1 week

We design the agent graph, select tools and retrieval strategy, and write the eval suite before a single line of agent code.

03

Build, Eval, Iterate

4-6 weeks

We build in sprints with evals running on every commit. No feature ships without passing its test suite.

04

Launch & Operate

Ongoing

We own the agent post-launch: monitoring, retraining, cost optimisation, and incident response included.

Let's Build Together

Ready to Build Your Intelligent System?

Talk to an AI Architect. Get a scoped proposal within 48 hours. No commitment required.

  • Response within 24 hours
  • Dedicated AI Architect assigned
  • Free architecture consultation

What Happens Next

We review your request: within 24 hours
AI Architect assigned: dedicated to your case
Scoped proposal delivered: no commitment required

Start the Conversation

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What Happens Next?

01

We Receive Your Query

02

Analyze Request Details

03

Arrange Consultation Call

04

Propose Tailored Solutions