Agentic AI across the software lifecycle

Quality is decided long before the tests run.

By the time a suite goes green, most of the cost is already committed — in an ambiguous requirement, an undocumented decision, a review that never checked the code against what was asked for. We build agentic AI that works inside the tools your teams already run, planning and acting and verifying across every phase, so the gaps between them stop leaking quality.

See the gaps we close
What makes it an agent
PlansReads the specification and works out the steps, rather than waiting to be told each one.
ActsWorks inside Jira, your repository and your pipeline — not in a chat window beside them.
VerifiesChecks its own output against the acceptance criteria before a person is asked to look.
ReportsLands as a pull request, a comment or a change record, on your ledger and in your review.
Agentic AI across your delivery lifecycle Select a phase
Where quality leaks

What the agent closes

We meet your stack where it already is.

Agents are built against the tools you run today — your repositories, your ticketing, your model tenancy. There is no new platform to buy, migrate to, or get approved.

Models01
Claude
ChatGPT
Gemini
Llama
AI platforms02
AWS
Bedrock
Azure
AI Foundry
Google
Vertex AI
Repositories & CI03
GitHub
GitLab
ADAzure
DevOps
Bitbucket
Infrastructure04
Docker
Kubernetes
Terraform
Delivery & ITSM05
Jira
Confluence
SNServiceNow
Slack
Microsoft
Teams
Languages & runtimes06
.N.NET
Java
Python
React
CBCOBOL

The agents we build.

Every engagement starts with your lifecycle, not our catalogue. These are the seven places we most often find quality being lost, and the agents our engineers build, inside your environment, to hold them.

01

Requirements intelligence

Reads epics and specs the way a senior engineer would: extracting acceptance criteria, flagging what is ambiguous, and holding a traceability line from the requirement to the code that satisfies it.

Works in Jira, Confluence, Azure Boards
Ambiguity in requirements
02

Implementation agents

Builds to your specification inside your architecture — matching the conventions already in the repository rather than importing a house style your team then has to unpick.

Delivers as reviewable pull requests
Matching repository conventions
03

Review & compliance

Checks the diff against the requirement it was meant to satisfy, not just against style rules — and attaches the evidence your auditors will ask for six months from now.

Works in GitHub, GitLab, Bitbucket
Salesforce conflict resolution
04

Verification & quality gates

Coverage measured against acceptance criteria rather than lines. Contract, performance and access gates run alongside the suite, so a green build means the change is right — not just that it executed.

Runs in your existing pipeline
Green builds, missed criteria
05

Workflow orchestration

Automates the handoffs that sit between teams and tools: change records raised from merged scope, status kept honest, the right people notified with the context already assembled.

Works in ServiceNow, Slack, Teams
Assembling the change record
06

Legacy modernization

Reconstructs behaviour from legacy source where the documentation is gone, maps it onto a modern target, and generates the parity harness that proves nothing was quietly dropped.

Review past project COBOL → .NET
07

Generated-test assurance

Suites written by a model arrive faster than anyone can read them. The agent reviews the generated tests themselves — catching the scenario whose data contradicts its own name, before it becomes a green tick nobody thinks to question.

Built as API Test Generator and Execution
When a green test proves nothing

How we work

Forward deployed engineering.

Our engineers work inside your environment, not ours. They join your teams, in your tools, under your process — and build agents against the reality of your stack rather than against a reference architecture.

What we leave behind is running agents, not a set of recommendations.

01
Embed Engineers join your teams, in your tools, under your process — not a parallel workstream that has to be reconciled later.
02
Instrument We map where quality and time are actually lost across your lifecycle, so the work targets your gaps rather than generic ones.
03
Build Agents built to your stack, your conventions and your acceptance criteria, reviewed the way your own engineers' work is reviewed.
04
Hand over The agents keep running in your pipeline. Your team owns them, changes them, and does not need us to keep them working.

Security

Scoped by design.
Not by promise.

Most AI tooling asks for broad access and offers assurances about what it will do with it. Our agents work the other way around: they hold a service identity in each tool with exactly the permissions you grant, and nothing beyond them. What an agent cannot reach, it cannot affect.

Service identity, never adminEach agent authenticates as its own scoped account in each tool. Permissions are granted per project, and revoked in one place.
Your model, your tenancyAgents run against the model endpoint you nominate — your Bedrock, AI Foundry or Vertex account — so prompts and code stay inside your boundary.
Auditable by defaultEvery action an agent takes lands as a comment, a commit, a pull request or a change record. There is no off-ledger work.
No standing infrastructure accessNo production credentials, no shell, no direct database access. Agents propose changes; your pipeline and your people apply them.
Example access scope · single engagement
Jira projectRead, comment
RepositoryBranch, open PR
CI pipelineRead results
Model endpointYour tenancy
Production shellNot granted
Cloud adminNot granted
Customer data storesNot granted

Start with the phase that hurts most.

Tell us where work slows down or quality slips, and we will show you the agent we would build for it — against your tools, on your terms.