Solutions
AI Agent Development
Our agents are already running in production. They produce results you can verify against the source, and they are operated like professional systems: evals, monitoring, cost control.
Evals from day one · AWS or GCP · Your runtime, your data
Production, not demos
The demo is the easy part.
Most agent projects die between the demo and the first real users, who need the same question answered the same way every time. They need results they can verify against the source and change when the business changes, and that gap is the engineering we do.
- Auditable by defaultEvery tool call, source and agent decision stays traceable.
- Measured before launchGolden datasets and eval suites gate changes before production.
- Operated like softwareLatency, token cost, failures and drift are visible to your team.
The stack we build on
Proven platforms, wired together by us.
Framework, observability, models and cloud. Every part is one your team can staff, audit and take over.
Agent framework
Typed tools, workflows, memory and evals in one TypeScript framework.
Observability
Traces, token spend and latency per workflow, plus eval runs.
Model platform
Claude for the frontier reasoning steps of a workflow.
Model platform
Swapped in per task, so no single vendor owns the system.
Cloud
Advanced Tier Services Partner. Agents run in your own account.
Cloud
The same deployment model when your data already lives in GCP.
What we build
We deliver the complete professional harness around the LLM.
The model is one component. Everything that makes it dependable at work is engineering: tools, memory, data, evals and operations.
Foundation
Agent tools
Typed toolkits over your APIs, databases and metrics.
MCP servers
Your internal systems exposed to any MCP-capable agent or IDE.
APIs and data
The integration work that makes an LLM useful against real systems.
Intelligence
Memory systems
Episodic and semantic memory, so the agent improves across sessions.
Skills and workflows
Multi-agent flows with explicit control, not one hopeful prompt.
Model routing
Cheap steps to small models, hard reasoning to frontier ones.
Operations
Evals and datasets
Golden datasets that gate every prompt, tool or model change.
Observability
Langfuse traces plus Mastra monitoring, per workflow.
Cost and latency
Context engineering that cuts spend and response time together.
Reliability engineering
Quality, cost and latency are one system.
Optimising one lever in isolation creates surprises elsewhere. We measure the whole runtime: groundedness, task success, spend, response time and model drift.
The right question is not "which model?" It is "what do we need to trust this workflow?"
Production readiness
Measured- EvalsTask success and groundedness, run on every change.
- Golden datasetsRepresentative cases curated before launch.
- Token costSpend visible per workflow and per user.
- LatencySlow steps traced to the tool or model responsible.
- ObservabilityPrompts, tools, memory and errors, all traced.
How an engagement runs
From audit to on-call, without a handoff gap.
01Automation audit
We map your workflows and identify what agents can automate cheaply, with cost and effort estimates per candidate.
Output: a ranked candidate list
02Pilot with evals
One scoped agent, a golden dataset, an eval suite. You see measured accuracy before committing further.
Output: measured accuracy
03Production hardening
Monitoring, error handling, cost controls, security review, deployment into your AWS or GCP account.
Output: an agent live in your cloud
04Monitored operation
Langfuse dashboards, eval regression gates, and iteration on tools and memory as usage grows.
Output: no silent regressions
Each phase has a stop condition: audit before pilot, measured accuracy before hardening, hardening before anything carries production load.
Our Work
Proof, not promises.
Harmonyze - AI
From contract AI to the coaching command centre for franchise brands
An AI teammate for contract and compliance work that delivered a 10x ROI, and the engineering now behind a coaching platform for franchise brands.
AI powered scalability
Automated data conversion to scale elite CrossFit programming
Reduced training plan creation time by automating freeform-to-structured data conversion, unlocking scalability for elite CrossFit programming
Cloud-Native Acceleration
Production-grade MVP of StarOps in customer pilots within months
Delivered a production-grade MVP of StarOps in customer pilots within months, without requiring the client to build a large in-house platform team.
Frequently asked questions
The questions engineering leaders ask first.
Clear answers before a discovery call.
What framework do you use to build AI agents?
Our default is Mastra, a TypeScript agent framework we adopted early and know deeply. We have also built custom harnesses and LLM SDKs where a framework did not fit. Our systems blend and swap models across OpenAI, Anthropic and Google, so you are never locked to one provider.
How do you keep token costs and hallucinations under control?
Every agent we ship is instrumented with Langfuse and Mastra internal monitoring, so token spend and reliability are visible per workflow, not just per invoice. We apply context engineering, controlling what enters the model's window at each step, which improves accuracy while lowering spend and latency. Evals against golden datasets catch regressions before users do.
Can you deploy agents inside our AWS or GCP account?
Yes, that is our standard model. We deploy into client-owned AWS and GCP accounts, so nothing sensitive leaves your infrastructure and your team owns the runtime from day one. As an AWS Advanced Tier Services Partner we handle the account-level setup, IAM and networking included.
Do you build evals and golden datasets for existing agents?
Yes. We have built golden datasets and eval suites for our own products and for client systems, including agents we did not originally write. An eval suite is usually the first thing we add during an audit, because you cannot harden or extend an agent you cannot measure.
How long until an agent is in production?
It depends on integration surface, but our track record is months, not years. We delivered a production-grade MVP of StarOps, an agent-driven cloud delivery platform, into customer pilots within months. A scoped pilot with evals typically lands within the first few weeks of an engagement.
Got something hard to ship?
Bidders, multiplayer infra, agentic platforms, or all three, tell us what you're building.