Product & software engineering for regulated enterprises
Put AI agents on every stage of your delivery pipeline
Most AI budgets go to the coding step, which was rarely the slow part. We put an agent on every stage instead: choosing what to build, building it, testing, security, release, and keeping it running afterwards. Your people still make the calls.
The agents run on tools you license yourself, direct from the vendor. We hold no reseller agreements and take no margin on anything we recommend. The architecture we assemble them into is published in full, so you can judge it before we speak.
One request, top to bottom. Every layer is a third-party product licensed in your name.
40–70%
Cycle time removed
depends on your baseline
2–3×
More shipped per squad
usually by month four
6 wks
To first agent in production
if your CI's already green
Zero
Licenses sold by us
you license direct
Ranges from our own engagements. But they vary, and on a first value stream the low end is more common than the high end. We measure your actual baseline in week one.
What we bring
The short version
You are hiring an engineering firm, not buying a platform. So here is what changes once we start, in plain terms.
Your existing teams go faster
Agents do the drafting: requirements, code, tests, pipelines, release notes. Engineers review and decide, and nothing merges without a person reading it.
We buy nothing on your behalf
Every tool sits on a license in your name, bought direct at your own rate. No reseller deal and no margin, so there's nothing to unwind if you drop us.
You keep all of it at the end
Source, runbooks and architecture are handed over as we go. If we walked out in month six, what we built would keep running without us.
How we run delivery
An agent on every stage, not just the coding one
Each of the eight stages below gets an agent that owns an output, not a suggestion box. The products named underneath are what we reach for most often. They get swapped for whatever you already run, or whatever your procurement will actually approve.
01 DiscoverProduction feeds back into discovery. Tap a stage to open it.
Stage 01 — Discover
Product Intelligence Agent
Reads product telemetry, support tickets and revenue data, then ranks what looks worth building.
Worth knowingReplaces the workshop cycle. You still make the call.
Opportunity briefs ranked by value, quotes attached
Build / buy / partner recommendation
Common defaultsAmplitudeProductboardZendesk
Why iNexCCo
Why bring in anyone at all
Every tool we use is one you could buy yourself. You're paying for the integration and for the judgment about which pieces fit, with no resale margin riding on the answer. Each of the other models beats us at something: consultancies have far more people, copilots cost less and install in a minute, platform vendors give you one throat to choke.
Capability
iNexCCoIntegration-led engineering
Global SI / AI ConsultancyAdvisory-led delivery
AI Coding AssistantsIDE copilots
AI Platform VendorsOwn-stack platforms
Covers the full lifecycle, discovery through operations
No proprietary platform of ours to get locked into
Useful on day one with no integration workA copilot license is live in a minute and worth buying regardless of us. The integration is what makes the other seven stages work.
Every tool license held directly by you, in your name
Tooling advice with no resale margin behind it
Works with the tools you've already bought
A bench you can scale to a hundred engineers next quarterWe're small on purpose. If headcount is the requirement, an SI is the right call and we'll say so on the first call.
Agents accountable for outcomes, not just suggestions
Governance and audit wired in from the first commit
Runs on-premises or fully air-gapped
One vendor accountable for the whole stackYou get one integrator, not one licensor. The trade is that no single contract can hold your renewal hostage.
Priced against outcomes, not billable headcount
Architecture and runbooks handed to your team
Swipe to compare all four columns →
Yes, as delivered Partial, or only with extra work Not addressed
Compares delivery models, not named firms, from what each publicly documents. Vendors vary widely inside a single column.
Reference architecture
What the stack is made of
Six layers, and we wrote none of them. Every box is a product you can buy today and drop later on your own terms. What you pay us for is knowing which ones fit together, and doing the integration. We bring our own accelerators so you are not starting from an empty repo, and you get their source. The names below are examples, not requirements.
L6
L5
L4
L3
L2
L1
Scrolling pulls the stack apart one layer at a time. Hover a row to light its plane.
L6
Experience
Where engineers actually meet the agents: portal, chat, review queues.
BackstageSlackJira
L5
Orchestration
Multi-agent planning and long-running workflows that survive a restart. It's where most home-grown attempts fall over.
LangGraphTemporalAirflow
L4
Models & Tools
A gateway in front of the providers so you can switch, plus sandboxed tool execution and an MCP registry.
Anthropic ClaudeOpenAIAWS Bedrock
L3
Governance & Trust
Policy-as-code on every tool call, secrets kept out of prompts, an audit trail, and a kill switch that works. We spend most of our time here.
Open Policy AgentHashiCorp VaultWiz
L2
Data & Integration
Agents reach your systems of record without a six-month data project first.
KafkaAirbytepgvector
L1
Deployment
Runs where your data already lives, whether that's cloud, on-prem, hybrid, or air-gapped.
KubernetesTerraformArgo CD
System overview
What one request actually touches
The layers above are what you buy. Here's what happens when a request runs through them. Every hop can refuse it, redact it, or hand it to a person. Wiring that in is most of the work. We call the assembled blueprint VelocityOS (documentation, no license fee); the licenses under it stay yours.
Governance and trust
Checks every hop below. Fails closed when it can't decide.
Identity on every call, including agent-to-agent
Policy evaluated before the action runs
Guardrails on the prompt in and the output back
A kill switch that revokes tokens in seconds
01
Surface
An IDE prompt, a Slack message, a Jira transition, a webhook.
02
Intake
Deduplicated, stamped with the caller's identity, and written to durable state before anything runs.
03
Context
Retrieval across your repos, tickets and docs, filtered by what the caller could already see. Sources stay attached to the answer.
04
Model and tools
One gateway in front of every provider, so switching model is a config change. Tool calls run sandboxed.
05
Result
Output scanned on the way out, then logged in full.
Where a person gets pulled in. Anything destructive, over budget or low-confidence stops between 04 and 05 and waits for approval. The workflow is durable, so a twelve-minute pause costs what twelve seconds costs. The full path runs to fourteen hops. What each one can refuse is written up on the reference architecture page.
Telemetry
Runs back the other way. Every hop reports, one place reads it.
Traces scoped to a run across every service
Cost attributed to the team that spent it
Quality scored against a held-out set
All of it runs on your infrastructure, under licenses in your name. Cloud, on-prem, hybrid, or air-gapped.
Most enterprise AI work dies between pilot and production, so every phase below ends with something running. Timings assume repo access in week one (the usual bottleneck).
01 Weeks 1–2
Delivery Readiness Assessment
We map one value stream end to end and baseline your DORA and flow metrics from your git history. If you're not ready, we say so now rather than six months in.
Value stream map, waste quantified
DORA and flow baseline
Agent-readiness scorecard
Costed 90-day plan
02 Weeks 3–6
Lighthouse Build
Agents running against your real code and pipelines, governed from the first commit. Retrofitting governance later is miserable.
Agents live on one value stream
Golden path and guardrails
Measured delta against baseline
03 Weeks 7–12
Industrialize
Turn the one working thing into something other squads can pick up without us.
Reusable agent templates
Policy-as-code controls
Reference architectures
Platform runbooks
Engineer enablement
04 Quarter 2 onward
Scale & Hand Over
Rollout across squads, then a deliberate handover. We'd rather you stopped needing us.
Agent FinOps and cost attribution
Continuous eval harness
Full IP and ops handover
When we are the wrong call
We'd rather lose the deal than the first six months. If your builds are red more often than green, agents just generate failures faster. If nobody owns the value stream, no one accepts the agents' output and the work stalls in review. Under fifty engineers the platform overhead isn't worth it, and a good copilot license gets you most of the way. And if what you need is someone to tell the board that AI is happening, hire a consultancy. They are genuinely better at that than we are.
Start with the assessment
Two weeks, one value stream, a costed plan for your board. Priced per engagement once we've seen the scope. Ask and the number comes back before any call.
Mostly regulated industries, because that is where the delivery constraints are real and where doing this carelessly gets expensive. The lifecycle stays the same; the controls change.
Banking & Capital Markets
Core modernization under regulatory scrutiny, every agent action logged.
Representative use caseCOBOL-to-Java migration with regression parity proof
Insurance
Faster underwriting and claims cycles, controls intact.
Representative use caseClaims agents with human adjudication gates
Healthcare & Life Sciences
Validated releases under HIPAA, GxP and 21 CFR Part 11.
Representative use caseValidation evidence generated across the SDLC
Retail & Commerce
Production signal into the next release, within days.
Representative use caseDiscovery agents feeding merchandising releases
Manufacturing
OT-adjacent systems, where downtime is priced by the second.
Representative use caseAir-gapped release orchestration at the edge
Logistics & Supply Chain
Planning and visibility rebuilt around live decisioning.
Representative use caseException agents wired into carrier and WMS systems
Technology & ISV
Ship faster than better-funded rivals.
Representative use caseMulti-repo agent fleets
Telecom & Media
Large estates, where delivery has to heal itself.
Representative use caseRoot cause analysis across multi-vendor stacks
Send us your constraints and we'll say honestly whether we fit. Get in touch
Solutions
Beyond the lifecycle
The same architecture that delivers your software runs the operations around it, so delivery and business agents end up sharing one control plane.
Agentic Automation
Business processes run by agents, with approval gates where the risk sits.
Process Mining
Autonomous Task Execution
Human-in-the-Loop Approvals
Exception Routing
AI Security Operations
Agents work the alert queue overnight and escalate what needs a human.
Threat Detection & Response
Vulnerability Triage
Compliance Evidence
Cloud & Infrastructure
AWS, Azure and GCP, with agents on scaling, spend and migration waves.
Multi-Cloud Orchestration
Migration Wave Automation
DevOps Intelligence
CI/CD, Terraform and release management. If your pipeline's already green, agents stay out of the way.
Pipeline Agents
Terraform Module Generation
Progressive Delivery
Data & Analytics
Pipelines, quality checks and the reporting nobody wants to maintain.
Pipeline Orchestration
Data Quality Monitoring
Enterprise Integration
Agents wired into the CRM, ERP and ITSM you already run, on the integration platform you're already paying for.
Legacy System Integration
Event-Driven Architecture
API Gateway Management
How We Work
Things we'll put in writing
You get working software
Every engagement ends with software running in your environment, on licenses you hold.
Controls on from day one
Agent firewall, non-human identity and policy-as-code from the first commit. Retrofitting that before an audit is painful, and we've watched people try.
Our accelerators come with source
We bring our own agent templates, policy bundles, evaluation harnesses and infrastructure modules so you're not starting from an empty repo. Terms are agreed in the contract, and you get the source to keep and change. Nothing we bring is a hook.
We plan our own exit
Your team keeps the platform, templates and runbooks. If you still need us in year three, one of us has done something wrong.
Ecosystem
We integrate, we don't resell
We hold no reseller agreements and take no margin on the tools we recommend, which is the only reason our advice on them is worth anything. You buy direct, and you can drop any layer without asking us.
Budget for the licenses separately. They're a real cost, they sit outside our fee, and moving fast usually increases them, because parallel environments and higher tiers are what buy you the speed. If you'd rather we placed the orders, we pass the vendor's price through untouched and earn nothing on it.
Cloud platformsModel providersData and streamingIdentity and secretsSource control and CIService management and ERP
Name the value stream that frustrates you and describe it. We'll scope a readiness assessment around that one thing and come back with a baseline and a price, usually within two working days. A real engineer reads these, not a form router.
A map of the value stream, with waiting time counted separately from working time
DORA and flow baseline, measured from your own git history
Where your data and tooling aren't ready
A costed 90-day plan, or a reason not to start yet