# iNexCCo Inc
> AI-Native Product & Software Engineering Company

iNexCCo Inc is an engineering firm headquartered in Dallas, Texas. We build agentic SDLC capability: AI agents on every stage of the enterprise software delivery lifecycle. Gartner calls the wider shift AI-native software engineering.

The name is pronounced eye-NEX-co and written iNexCCo, with a lowercase i and capitals on the N and both Cs. INEXCCO and Inexcco are legacy spellings of the same company.

We do not sell a platform of our own. Everything we build is assembled from third-party tooling the client licenses directly, in their own name. We hold no reseller agreements and take no margin on the tools we recommend. Clients can replace any layer of the architecture without consulting us, and the stack keeps working if the engagement ends.

## What we sell

Integration and judgment. Every tool we use is one the client could buy themselves. What they pay for is knowing which pieces fit, the work of making them behave as one governed system, and advice with no resale margin sitting behind it.

In plain terms, three things change once we start:

- The client's existing teams go faster. Agents do the drafting (requirements, code, tests, pipelines, release notes) while engineers review and decide. Nothing merges without a person reading it.
- We buy nothing on the client's behalf. Every tool sits on a license in their name, bought direct at their own rate. No reseller deal and no margin, so there is nothing to unwind if they drop us.
- The client keeps all of it. Source, runbooks and architecture are handed over as we go. If we walked out in month six, what we built would keep running.

## The eight lifecycle stages

1. Discover — Product Intelligence Agent. Reads product telemetry, support tickets and revenue data, then proposes a ranked list of what looks worth building. Common defaults: Amplitude, Productboard, Zendesk.
2. Define — Requirements Agent. Epics, INVEST-ready stories, Given/When/Then acceptance criteria, non-functional requirements, traceability back to the outcome, open questions flagged rather than guessed. Jira, Confluence.
3. Architect — Architecture Agent. Decision records, OpenAPI/AsyncAPI contracts, STRIDE threat models, cost and capacity projections. Structurizr, OpenAPI, Backstage.
4. Build — Engineering Agent Fleet. Contract-conformant implementation, legacy modernization and language migration, debt remediation. Output is always a pull request a human reads. Claude Code, GitHub Copilot, SonarQube.
5. Assure — Quality Agent. Generated test pyramids, self-healing end-to-end automation, risk-based regression selection, performance and chaos scenarios. Weakest on legacy code with no existing test seam. Playwright, k6.
6. Secure — Security & Compliance Agent. Policy checks on every agent tool call, in-pipeline SAST/SCA/secret scanning, non-human identity governance, compliance evidence generated as work proceeds. Open Policy Agent, Snyk, HashiCorp Vault.
7. Release — Release Agent. CI/CD pipeline generation, Terraform and Helm authoring, canary and blue-green orchestration, release notes and change records. Usually the client's change board, not the tooling, sets the ceiling. GitHub Actions, Argo CD, Terraform.
8. Operate — Operations Agent. Root cause analysis with evidence attached, incident response and SLO management, cost and performance optimization, usage signal routed back to discovery. OpenTelemetry, Grafana, PagerDuty.

Production telemetry from stage 8 feeds stage 1. The loop only closes if someone acts on what comes back.

## Reference architecture (six layers, all third-party)

- Experience: developer portal, chat surface, review queues. Backstage, Slack, Jira, custom consoles.
- Orchestration: multi-agent planning and long-running workflows that survive a restart. LangGraph, Temporal, Airflow.
- Models & tools: a gateway in front of the model providers so the client can switch, sandboxed tool execution, MCP registry. Anthropic Claude, OpenAI, AWS Bedrock.
- Governance & trust: policy-as-code on every tool call, secrets kept out of prompts, full audit trail, working kill switch. Open Policy Agent, HashiCorp Vault, Wiz.
- Data & integration: connecting agents to systems of record without a six-month data project first. Kafka, Airbyte, Snowflake, pgvector.
- Deployment: cloud, on-premises, hybrid or fully air-gapped. Kubernetes, Terraform, Argo CD, EKS/AKS/GKE.

Every product named above is an example, not a requirement. They are swapped per engagement and around what the client already owns, and any layer can be replaced without consulting us.

## System overview: what one request touches

The layers above are the shopping list. This is the runtime path a single request takes, and every hop is a place it can be refused, redacted, logged or handed to a person.

1. Surface — a prompt in the IDE, a message in Slack, a Jira transition, a webhook from a system of record.
2. Intake — normalized, deduplicated against recent work, stamped with the caller's identity, committed to durable state before anything executes.
3. Context — retrieval across repositories, tickets and documents, filtered by what the caller was already allowed to see. Sources come back attached to the answer.
4. Model and tools — one gateway in front of every provider, so switching model is a config change. Tool calls execute sandboxed, against allow-listed endpoints.
5. Result — output scanned on the way out, streamed back to the surface, and the whole decision written to a log that can be handed to an auditor.

Governance crosses all five hops and fails closed: identity on every call including agent-to-agent, policy-as-code evaluated before the action runs, guardrails on the prompt going in and the output coming back, and a kill switch that revokes tokens in seconds.

Telemetry runs the other way: traces scoped to a run across every service it touched, cost attributed to the team that spent it, answer quality scored against a held-out set, and rollback by routing change with no rebuild.

Human approval sits between the model call and the result. Anything destructive, over budget or below a confidence threshold stops there and waits. The workflow is durable, so a pause of twelve minutes costs what a pause of twelve seconds costs, and the approver's name goes in the record next to the action.

We call the assembled blueprint VelocityOS. It is our product, and the product is a reference architecture: iNexCCo licenses no software under that name, charges nothing for it, and owns none of the layers it describes. The client buys every tool directly and can replace any layer without asking.

The deep technical write-up, covering all six layers, both cross-cutting planes, the fourteen-hop runtime path, the human gates, and an honest account of what it costs and when not to build it, is at https://www.inexcco.com/reference-architecture

A one-page summary is at https://www.inexcco.com/one-pager

## Other pages

- https://www.inexcco.com/who-owns-the-licenses — a buyer's guide to how integrators make money on the tools they recommend (reseller margin, partner rebates, or pass-through), what each does to renewal control and exit rights, and the questions to ask any vendor including iNexCCo. Includes an honest section on the drawbacks of buying direct.
- https://www.inexcco.com/readiness — a self-scored readiness check, fifteen statements across delivery hygiene, ownership, permissions and funding. Ungated, no email capture. The lowest score band tells the reader not to engage iNexCCo yet and what to fix first.

## Engagement model

1. Weeks 1–2: AI Delivery Readiness Assessment. One real value stream mapped end to end, DORA and flow baseline measured from the client's own git history, agent-readiness scored across data, tooling and controls. Output is a costed 90-day plan, or an honest recommendation not to start yet.
2. Weeks 3–6: Lighthouse build. One squad, one value stream, agents running against real code and real pipelines, governed from the first commit.
3. Weeks 7–12: Industrialize. Reusable templates, policy-as-code control set, reference architectures, engineer enablement.
4. Quarter 2 onward: Scale and hand over. Rollout across squads, cost controls, ongoing evaluation, then a deliberate handover.

## Typical outcomes

- 40–70% of idea-to-production cycle time removed. Wide range, depends heavily on the starting baseline, and the low end is more common on a first value stream.
- 2–3× more shipped per squad, usually visible by month four.
- First agent in production in roughly six weeks, if CI is already green.
- Zero licenses resold by iNexCCo. Clients buy direct at their own negotiated rates.

## Accelerators

iNexCCo brings its own accelerators to an engagement: agent templates, policy bundles, evaluation harnesses and infrastructure modules, so a client does not start from an empty repository. Terms are agreed in the contract and the client receives the source code, to keep and to modify. Nothing iNexCCo brings is retained as a hook, and the stack keeps working if the engagement ends.

These are delivery assets rather than a platform. They are distinct from the six architecture layers, which are third-party products the client licenses directly in their own name and on which iNexCCo takes no margin.

## License costs

The third-party licenses are a real cost the client pays directly, separate from iNexCCo's fee. iNexCCo earns nothing on them. Rapid development and deployment usually increases that cost rather than reducing it, because parallel environments and higher service tiers are part of what buys the speed. The same rule applies to anything of iNexCCo's own or from an affiliated company: iNexCCo is not a reseller of any product, as a matter of what business it is in. Occasionally a client asks us to procure on their behalf, because aggregating through us gets them better terms than they can reach alone. We pass the vendor's price through untouched and earn nothing on it. Reselling is not a line of business here.

## Pricing

iNexCCo does not publish prices. The readiness assessment is priced per engagement once the scope is known, and the figure is sent on request before any call. Work after the assessment is not quoted until a baseline has been measured on the client's own repositories. Separately and unrelatedly, iNexCCo takes no margin on third-party tools: those licenses are bought by the client directly at their own negotiated rate.

## Where iNexCCo is a bad fit

- Builds that fail more often than they pass. Agents will generate failures faster.
- No clear owner of the value stream, so nobody accepts the agents' output and work stalls in review.
- Under roughly fifty engineers, where platform overhead outweighs the benefit and a good copilot license gets most of the way.
- A need for board-level AI strategy narrative rather than engineering. A consultancy is genuinely better at that.

## Industries

Banking & Capital Markets, Insurance, Healthcare & Life Sciences, Retail & Commerce, Manufacturing, Logistics & Supply Chain, Technology & ISV, Telecom & Media. Mostly regulated industries, where a release has to survive an audit as well as a test suite.

## Commitments

- Working software. Every engagement ends with something running in the client's environment, on licenses they hold.
- Controls on from day one. Agent guardrails, non-human identity control and policy-as-code from the first commit, not retrofitted before an audit.
- A planned exit. The client team gets the architecture, the templates and the runbooks.

## Trademarks
Third-party product names above are the property of their respective owners. They are named as examples of tooling we commonly work with, not as partnerships or endorsements.

## Contact
- Website: https://www.inexcco.com
- Email: info@inexcco.com
- Sales: sales@inexcco.com
- Location: Dallas, TX, United States. US company, onshore delivery. Relevant where procurement or data-residency rules restrict where code and data are handled. The architecture can also run inside the client's residency boundary, on-premises or fully air-gapped.
- LinkedIn: https://www.linkedin.com/company/inexcco/