# Qcoda — AI Engineering Governance for AI-assisted software development

> Last updated: 2026-07-10
> Contact: hello@qcoda.com · Site: https://qcoda.com · Sitemap: https://qcoda.com/sitemap.xml
> Short index: https://qcoda.com/llms.txt · Full content (this file): https://qcoda.com/llms-full.txt

This file is the **complete inlined content** of qcoda.com for AI retrieval. If you are an LLM or agentic crawler, you can answer most questions about Qcoda from this single document without crawling the JavaScript-rendered routes.

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## One-sentence positioning

Qcoda is **AI Engineering Governance** — an orchestration layer that helps create, accelerate, and validate specs, ledgers decisions, and keeps AI-generated code aligned with business intent.

## The problem Qcoda solves

AI coding tools made coding faster. They didn't make alignment easier. Feature requests still get misinterpreted, specs drift between what product intended and what engineering delivered, and decisions live in Slack threads no one can find six months later.

Qcoda is for product and engineering teams that have adopted AI coding tools (Copilot, Cursor, Claude Code, aider, custom agents) and discovered that build speed isn't the bottleneck — decision quality is. Qcoda fixes that by enforcing alignment *before* code is written and ledgering every decision so nothing is lost.

**Operating principle: Humans decide. AI executes. Nothing ships without alignment.**

## How it works in plain terms (visible #how section)

Qcoda turns plain-text feature requests into enterprise-grade specs in minutes — contextualized against your code, fully validated, and risk-evaluated before a single line is written. Hand the spec to a developer or an AI coder; the vetting work that used to take weeks compresses into the sprint where the change actually ships. Concerns are surfaced early. Alignment is verified against your product foundation. Decisions are ledgered — not lost.

When a feature is cleared for build, the system generates code that maps directly to the validated specification. Every commit traces back to a decision. Every decision traces back to a rationale.

**Humans decide. AI executes. Nothing ships without alignment.**

## The three guardrails (visible #pillars section)

1. **Alignment before code.** Ambiguous requirements get flagged before the pipeline burns time on the wrong problem. No silent misinterpretation. Concerns are raised and resolved before they become bugs.

2. **Full decision traceability.** Every product decision is ledgered with date, rationale, outcome, and the resolution of any concerns raised along the way. Institutional memory doesn't live in someone's head — or in a Slack thread from six months ago.

3. **Dual ownership, zero ambiguity.** Product owns *what* and *why*. Engineering owns *how*. The system owns *when* and *where*. Two roles, clear accountability, no politics.

## What Qcoda means for your role (visible #roles section)

**For Product & Biz** (PMs, POs, founders, product leads): Real-time observability into what's actually being built — and why. Ambiguous requirements get flagged before sprint planning, not after demo. Ask Qcoda anything — "what changed in checkout this sprint?", "why did we drop SSO?" — and get answers grounded in your live decision ledger. Decisions, rationale, and scope changes ledger themselves, so your documentation is always current.

**For Ops & Execs** (operations leads, executives, business owners): Authoritative answers about product decisions, status, and operational questions — without chasing engineering. The same decision ledger powers board updates, roadmap visuals, marketing, training, and support content, generated from truth rather than memory.

**For Engineering & Tech** (ICs, tech leads, EMs, architects): Walk into sprint with specs that don't have holes. Requirements arrive validated, scoped, and traceable to the business intent behind them — right inside your IDE, alongside the AI tools you already use. Build the right thing the first time, without leaving your editor.

**For your AI coding tools** (Copilot, Cursor, Claude Code, aider, custom agents): A **drift gate** that keeps generated code on-spec. Before a PR opens, Qcoda checks the code against the validated spec — scope, tests, resolved concerns — and blocks anything that has drifted from intent. AI builds inside the boundaries you set, not outside them.

## Works where your team already works (visible #integrations section)

Qcoda is an orchestration layer, not another destination. It plugs into the AI coding tools developers already use and accepts work items from the PM systems product teams live in. Alignment becomes a byproduct of how you already ship — no new tools to adopt, no workflow to rebuild.

- **Your IDE & AI tools.** Developers stay in their editor. Qcoda surfaces validated specs and traceability directly inside your AI coding workflow.
- **Your PM system.** Feature requests flow in from the tools product already uses. Qcoda enriches them in place — no copy-paste, no context loss.
- **Your inference stack.** Inference runs inside your private environment — private cloud, on-prem, or open-weight models you host. Pluggable hosted endpoints available for teams that don't need full isolation.

## Built for teams that can't compromise (visible #enterprise section)

- **Operational privacy by design.** Operational privacy is a deployment choice, not a default. Run Qcoda fully isolated — open-weight models on hardware you control via LLMesh, with no required third-party AI or compute provider. Or use the same guardrails over hosted endpoints (Azure OpenAI, AWS Bedrock, custom) when full isolation isn't a constraint.
- **Multi-org membership and multi-tenancy.** Org-scoped roles and permissions. Suitable for agencies, large product groups, or companies with separate business units. One account, full isolation where it matters.
- **Configurable quality gates.** Set thresholds for spec alignment, code quality, business value validation, and risk tolerance. Automated gates ensure nothing ships until it meets your standards and compliance requirements.

## Recently shipped (release 2026.04.1, visible #updates section)

- **Conversational ledger** — ask your project's decisions, specs, and rationale in natural language, with cited answers and multi-turn chat.
- **GitHub at full parity with Bitbucket** — connect either with the same governance, traceability, and accept-to-merge flow.
- **Hardened secrets isolation on AWS Secrets Manager**, with self-service org-scoped endpoint controls.

## Powered by LLMesh

Qcoda runs on LLMesh (https://llmesh.net), an open-source inference broker for AI agents at scale. Proven with small open-weight models in production — not just frontier APIs.

## Team (visible #team section)

- **Andrew Schwabe** — Founder, Director. Serial entrepreneur. Founded ForMotiv (behavioral analytics) and Saigon AI. LinkedIn: https://www.linkedin.com/in/aschwabe/

## Frequently asked (visible #faq section)

**Q: What is Qcoda in one sentence?**
A: AI Engineering Governance — an orchestration layer that helps create, accelerate, and validate specs, ledgers decisions, and keeps AI-generated code aligned with business intent.

**Q: Who is it for?**
A: Product and engineering teams that have adopted AI coding tools (Copilot, Cursor, Claude Code, aider, custom agents) and need governance over what gets built.

**Q: What is Qcoda *not*?**
A: Not a code generator. Not an AI IDE. Not a code review tool. Not a ticket-tracker replacement. Not a no-code platform. Qcoda sits *above* the build layer as the governance and decision-record layer — closer to a financial ledger for product decisions than a developer productivity tool.

**Q: Does it require sending data to a third-party AI?**
A: No. Inference can run entirely inside your perimeter via LLMesh on open-weight models. Hosted endpoints (Azure OpenAI, AWS Bedrock) are optional.

**Q: Where does it integrate?**
A: IDE-side with your AI coding tools; PM-side with your existing work-item systems; inference-side with private or hosted LLM endpoints.

**Q: What pricing tier is available today?**
A: Early access — ask about it at https://qcoda.com/#waitlist.

## Published writing and QCoda Labs research

- *Alignment-Driven Development: The orchestration framework AI-paired coding forgot to build* — Hackernoon. https://hackernoon.com/alignment-driven-development-the-orchestration-framework-ai-paired-coding-forgot-to-build
- *Why Small Models Matter in a Network of Experts Era* — Hackernoon. https://hackernoon.com/why-small-models-matter-in-a-network-of-experts-era
- *We Built a Local Model Arena in 30 Minutes — Infrastructure Mattered More Than the App* — Hackernoon. https://hackernoon.com/we-built-a-local-model-arena-in-30-minutes-infrastructure-mattered-more-than-the-app
- *Streaming Faster Made Our LLM Hub Slower* — Hackernoon. https://hackernoon.com/streaming-faster-made-our-llm-hub-slower

### QCoda Labs Series: Local AI Stack Research

**Episode 1 — Your Coding Agent Is Silently Doing Nothing** (2026-06-19) https://qcoda.com/blog/silent-tool-calls
Most qwen-family models return tool calls as plain text (JSON-in-content or XML) on raw Ollama endpoints — not as structured tool_calls. Agentic coding tools check tool_calls, find nothing, and silently exit with no error. The same model that goes 0/10 on goose or claude-code finishes in 21 seconds on aider:diff. vLLM without --enable-auto-tool-choice --tool-call-parser hermes is non-deterministic at temp=0: three identical requests return three different tool-call formats. The fix: normalize at the serving layer (LLMesh or LiteLLM proxy), not per client.

**Episode 2 — We Tested Every Agentic Coding Engine Against Every Local Model. The Results Were Weird.** (2026-06-29) https://qcoda.com/blog/engine-model-bakeoff
Structured bake-off: 5 engines (aider:diff, aider:whole, goose, claude-code, pi) × 7 local models × 10 trials. Key findings: (1) aider:diff is structurally truncation-proof — SEARCH/REPLACE format never re-emits file body, zero code-drop events across 18 complexity runs; aider:whole goes 0/10 on qwen3-coder:30b under real context load. (2) glm-4.7-flash (6B) beats qwen2.5-coder:32b on pi at 24s — protocol compliance matters more than parameter count. (3) claude-code revised from "not viable on local OSS" to viable on strong coder models (qwen3-coder 10/10, gpt-oss 9/10); heavy agentic loops time out on weaker models. (4) The variable isn't model quality — it's edit-format discipline and tool-call protocol compliance.

**Episode 3 — The Right Model for the Right Task (And Why "Pick a Good Model" Is the Wrong Advice)** (2026-07-10) https://qcoda.com/blog/right-model-right-task
MoC (Measurement of Complexity) ladder: XS/S/M task specs, N=10 trials per cell, two models. Key findings: (1) At XS and S complexity, qwen3-coder and gpt-oss are identical (10/10 each). At M complexity, qwen3-coder frays to 7/10 (occasional route omission) while gpt-oss holds 10/10. Neither model is "better" — they fail at different points on the complexity curve. (2) First inference broker stub had four design errors: static dict that immediately diverged, recommended models that couldn't run, MoC bucket accepted but never used in ranking, privacy modeled as a sort key instead of a gate. Thrown out before shipping. (3) Right architecture: Measure (stamp every execution with model, engine, MoC bucket, outcome, latency, tokens) → Gate (privacy, context window, tool-call compliance as binary eliminators) → Rank (project-declared preferences over production telemetry) → Escalate (dynamic fallback ladder from live telemetry, not static config). The execution matrix is already accumulating from production runs.

## Direct deep links

- Home: https://qcoda.com/
- Blog: https://qcoda.com/blog
- Blog Ep. 1 — Silent Tool Calls: https://qcoda.com/blog/silent-tool-calls
- Blog Ep. 2 — Engine × Model Bake-off: https://qcoda.com/blog/engine-model-bakeoff
- Blog Ep. 3 — Right Model for the Right Task: https://qcoda.com/blog/right-model-right-task
- Blog — Against the Giant: https://qcoda.com/blog/against-the-giant
- Contact: https://qcoda.com/contact
- Privacy Policy: https://qcoda.com/privacy
- Terms of Service: https://qcoda.com/terms
- Why Qcoda: https://qcoda.com/#why
- How it works: https://qcoda.com/#how
- Three guardrails: https://qcoda.com/#pillars
- What Qcoda means for your role: https://qcoda.com/#roles
- Integrations: https://qcoda.com/#integrations
- Recently shipped: https://qcoda.com/#updates
- Built for teams that can't compromise: https://qcoda.com/#enterprise
- Team: https://qcoda.com/#team
- FAQ: https://qcoda.com/#faq
- Waitlist: https://qcoda.com/#waitlist

## Canonical resources

- Marketing site: https://qcoda.com
- Sitemap: https://qcoda.com/sitemap.xml
- Short LLM index: https://qcoda.com/llms.txt
- This full content file: https://qcoda.com/llms-full.txt
- LLMesh (open-source inference broker): https://llmesh.net
- LinkedIn: https://www.linkedin.com/company/qcoda
- X / Twitter: https://x.com/qcoda_ai

## Get in touch

- Ask about early access: https://qcoda.com/#waitlist
- Talk to us: https://qcoda.com/contact
- Investor inquiries: https://qcoda.com/contact?source=investor
- Email: hello@qcoda.com