Our process

How we build production-ready AI agents.

Five disciplines, one team: discovery, architecture, development, testing, and operations. We've taken agents to production in insurance, fintech, logistics, and HR tech.

The production challenge

Getting to 70% is easy. Getting to 99% is a different job.

The gap between a weekend prototype and something reliable enough to run your business is wider than most teams expect. Closing it is our specialty.

70%EasyPrototype on a good dataset

90%HardHandle messy, real-world inputs

99%Super hardEnterprise-grade reliability

The capability gap

AI use cases are growing faster than teams can deliver them.

Every quarter, new models unlock new workloads. Most internal teams can't keep up with the rate of change — and the backlog of high-ROI agent ideas keeps growing.

Use cases ↑

Every new LLM release (context length, reasoning, tool use) unlocks workloads that weren't viable six months ago.

Capacity ↓

Building, evaluating, and operating agents in production is a specialised skill set that's scarce on the market.

The gap

Backlogs of valuable agent work grow — while the team stays heads-down on last quarter's prototype.

Autonomy vs reliability

The trade-off every agent team negotiates.

More autonomous agents do more work with less supervision — but are harder to trust. More deterministic workflows are predictable — but miss the ambiguity where real value hides.

DeterministicHigh reliability · narrow scope

AutonomousBroad scope · needs guardrails

Our job is to find the right point on this curve for each workflow — and to push it toward autonomy as models and evals improve.

Skip the hard part

Let us handle the hard part.

Get a personalized architecture + rollout plan from our team — free. We'll map your data, tools, and reliability targets to the right stack.

Our process

From idea to production in five disciplined steps.

Discovery & Assessment
We map your workflows, data, and existing systems. Identify the highest-ROI agent use cases and agree on success metrics before anyone writes code.
Architecture & Design
We design the agent graph — planning, tools, memory, guardrails, evals — and pick the right LLMs, vector DBs, and orchestration layer for your constraints.
Development & Integration
We build in tight iteration loops with your team, integrating with your CRM, ERP, ticketing system, and internal APIs behind feature flags.
Testing & Productionisation
We run agent evals against your real data, shadow-mode the agent against your current process, and harden for cost, latency, and failure modes.
Deployment & Monitoring
We ship to production with observability, rollback, cost controls, and continuous evals — so reliability keeps climbing after launch.

Technology stack

The tools we use to build reliable agents.

We're pragmatic — we pick the best tool for the job and integrate cleanly with what you already run.

Orchestration

LangGraph
LangChain
LlamaIndex

LLMs

OpenAI
Anthropic Claude
Google Gemini
Llama / Mistral

Vector DBs

Pinecone
Weaviate
Chroma
pgvector

Memory & state

Redis

Workflow

Temporal

Runtime

Modal
AWS Bedrock

Evals & observability

LangSmith
Arize

Ops

Datadog

Why enterprises partner with us

Three blockers we remove on day one.

Off-the-shelf AI is not customizable enough

ChatGPT, Copilot, and Notion AI don't know your data or your rules. Agents only deliver value when they're built around your context.

RPA is outdated for modern work

Brittle, script-heavy, and fragile to UI changes. Agents reason about the goal and adapt when things shift — without re-authoring scripts.

In-house teams lack the right competencies

Production agents require LLM, RAG, orchestration, evals, and SRE expertise layered together. We bring that capability in on day one.

Start building your AI agent today.

A free assessment, a written plan, and a team that's shipped agents into production many times over.

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Everything you need to know, from fundamentals to production.

OverviewAI Agents for EnterpriseProduction-ready AI agent services for enterprises. Orchestration, deployment, and productionisation.What Are AI AgentsWhat Are AI Agents?A complete technical guide to AI agents — LLMs, vector databases, prompting, RAG, plugins, and planning.Enterprise Use CasesEnterprise Use Cases100+ AI agent use cases that transform enterprise productivity across eight core areas.Get AssessmentFree AI Agents AssessmentComplete this 2-minute assessment and our team will prepare a personalized AI agent strategy for your business.