get in touch
services/ai agentic development

your AI agentic software partner.

We design, build, and operate AI agents and the software around them, from proof of concept to production. Deep expertise in Anthropic Claude, AWS Bedrock, and the Model Context Protocol, backed by 13+ years of full-cycle product engineering.

Book a discovery callhow we work
the gap

Most companies have run a pilot.
Few have agents in production.
We close that gap.

We build AI agents that plug into your real systems, follow your business rules, and deliver measurable outcomes. We start with a free proof of concept, so you see working software before you commit.

what we deliver.

01

Autonomous & semi-autonomous agents for business workflows: underwriting support, document processing, customer operations, engineering productivity.

02

MCP server development: connecting Claude and other models securely to your internal tools, data, and APIs.

03

RAG systems that answer from your knowledge, with citations and guardrails.

04

Agent evaluation, monitoring, and continuous improvement pipelines.

05

AI enablement for your teams: Claude Code adoption, workflow automation, prompt & context engineering.

the stack.

headline technologies
Anthropic ClaudeModel Context ProtocolAWS BedrockLangGraphAmazon SageMaker
show full stackhide full stack
Claude APIClaude Agent SDKClaude CodeBedrock Agents & AgentCoreOpenAI GPTGoogle GeminiLangChainOpenSearch
how we build · five phases, one loop

the agentic sdlc.

Agents run the build, people own the gates where judgment matters, and every release feeds what we learn back into the next one.

01/feature

specify

Stakeholder interviews, spec + acceptance criteria

UNDER THE HOODspec.md + acceptance/*.feature + success criteria
HUMAN GATEPM/BA confirms the spec traces back to validated evidence
02/design

design

Two tracks, in parallel

UX TRACKDetailed flows, states, usability checksFinal designs + UX context the agents build from
TECH TRACKArchitecture, key technical decisions, securitySystem design + a record of every key decision
HUMAN GATEDesigner signs off the UX checkpoint; Senior Developer approves the architecture
03/implement

build

Agent-run: plan, implement, test

plansubagent TDDe2e wiring
UNDER THE HOODplan.md, code + tests, 1 commit/task
AGENT CHECKSuite green, no blocked tasks. Human sign-off only for large or high-risk changes
04/implement

verify

Code, experience and AI behaviour

UNDER THE HOODreview-report.md, ux-review.md, approved PR
HUMAN GATESenior Developer + Designer are final approvers
05manual

ship + learn

Deploy, observe, measure use, retro

UNDER THE HOODmerged PR, adoption metrics, context updates
HUMAN GATEProduct Owner accepts the outcome, measured by use
production learning flows back into specs and UX context

five phases, four human gates. each phase produces a named artefact; each human gate is owned by a named person.

HUMAN GATEowned by a named person
AGENT CHECKautomated, escalates when risk is high

Gates scale with risk: low-risk changes pass specify, verify and ship; high-risk work passes every gate plus security review.

the difference

the most expensive work, moved earlier.

Traditional delivery discovers ambiguity during implementation, when it is most expensive to fix. We move clarification, validation, and test-first execution to the front — where agents excel and mistakes are cheap.

traditional delivery
Requirements arrive with gaps; edge cases surface mid-build.
Specs make assumptions about the system that no one verifies.
Tests are added at the end, if there's time.
Reviews are uneven — tired eyes, boilerplate treated like risk.
"Done" is a claim.
agentic delivery
Gaps are surfaced as questions in a depth-first interview, before any code.
Every claim about your system is checked against the real codebase.
Tests are written first, then wired to a real browser-driven run.
Every task is reviewed by an independent agent, then a human.
"Done" is a passing test you can point to.
proof

We outgrew our forecasting tool, so we built our own.

Agenticly: an agentic financial-forecasting product built in-house on Claude and the agentic SDLC, from proof of concept to production.

read the story

how we engage.

01

free proof of concept

We build a working agent or integration against your real use case. No cost, no commitment. You evaluate outcomes, not slideware.

02

milestone-based delivery

Production build-out priced against agreed milestones and success criteria. You pay for results.

Longer horizon? Dedicated teams: long-term product engineering and AI enablement capacity, embedded with your organization.

questions

the questions clients actually ask.

are ai agents writing the code that ships to production?
They draft it. A human reviews every diff and merges every PR. Specialist agents do the first-pass review — per-task and feature-wide — but final approval is always human, and a pre-commit hook physically blocks commits to main. We do not auto-merge.
what happens if an agent makes a mistake?
The same thing that happens when a human does: another reviewer catches it. The workflow has multiple lenses for exactly this — per-task review, acceptance-test wiring, feature-wide review, an optional architect-level review, and the human. Where we've seen mistakes, they've been caught at one of these layers.
what about ip and confidentiality?
We use enterprise-grade agent platforms with contractual protections: no client data is used to train third-party models. Specifics depend on the engagement and your tooling preferences, and we walk through them with you in onboarding.
what if the methodology doesn't fit our codebase?
It adapts. The agentic harness is tuned per project and mirrored into whichever coding tool your team uses day-to-day. The skills name real file paths from a scan of your stack, so they only work because they're configured to you. The difficulty triage is calibrated to your team and the maturity of your codebase.
what if we already have an sdlc?
We map onto yours. Our three pipelines replace — or augment — your discovery, design, and build-and-review phases, including the acceptance testing you'd otherwise run separately. Most clients find the overlap is large; the difference is in how each phase runs, not which phases exist.
what about regulatory or compliance work?
The audit trail this workflow produces is unusually strong: every decision written down with rationale, every claim verified before code, every acceptance scenario mapped to a passing test, every accepted task a real commit. For regulated industries we add explicit compliance gates at design and before merge.

start with an hour.

A discovery call, then a free proof of concept. You evaluate outcomes, not slideware.

Book a discovery call