CogniDev — A Structured Intelligent Development Environment.
An environment that augments Agentic AI–driven software development with structural, project-wide context.
Today's IDEs and AI platforms — VSCode, Cursor, and the rest — only hand you a box to prompt the LLM. They give you tools and options to ask the model, but they fail to give it any structural, project-wide context about the system it's actually editing.
And they ship no out-of-the-box utilities to catch the failures that quietly break real projects:
- Version drift — dependencies, frameworks, and runtimes silently sliding out of sync across the codebase.
- Breaking changes — API and signature changes that ripple through callers no one thinks to re-check.
- Architectural violations — generated code that ignores the boundaries, layers, and patterns the project already follows.
- Broken lineage — no trace from a change back to the structural decision that should have justified it.
CogniDev closes that gap. It wraps the model in a live dependency graph and a structured project model — so every suggestion is grounded, every reference resolves, and every drift is flagged before it reaches production.
Deterministic.
Traceable.
Architectural.
Vibe coding is breaking production.
CogniDev is how teams unbreak it.
AI app builders ship fast and fail loud. We rescue the apps, harden the architecture, and put audit-grade evidence behind every change.
See rescue playbooks →Playbooks for the work that actually ships.
Code generation is the easy part. Shipping migrations, modernizing stacks, passing audits, and rescuing vibe-coded apps before they break in production — that's the work teams need done. SI + AI delivers verified output, not vibes — deterministic structure, 1:1 traceability, audit-grade evidence at 60–80% the cost.
Service-as-a-Software for engineering.
YC's Summer 2026 RFS named it. Sequoia framed it. We've been building it. Companies that do the work, not sell another tool that helps a person do the work. CogniDev is that model for the engineering services pool.
Where the money actually is.
For every dollar a company spends on dev tools, it spends roughly six on the engineering services that deliver the work — modernizations, migrations, compliance prep, code rescues, audit packs. SaaS captured the dollar. SIs and consultancies captured the six.
A playbook is a priced outcome that targets the six.
What we own. What you keep.
- Production work — repeatable execution. Filing, drafting, generating, posting. We own this.
- Pattern application — known problem, known playbook. Onboard, reconcile, harden, audit. We own this.
- Strategic direction — pick the problem, frame the engagement, decide what to refuse. You keep this — it's getting more valuable.
Why pure-AI agents stall here.
Every enterprise survey says the same thing — hallucinations, human oversight, and audit defensibility are the binding constraints in regulated services. That's why generic AI agents stall in healthcare, finance, compliance, and audit-bound engineering work.
SI is the trust layer. Deterministic structure. 1:1 traceability. Audit-grade evidence on every run. The reason a regulated buyer will accept the agent.
Playbook ≠ Skill ≠ Plugin ≠ Agent.
The AI world ships four very different primitives under similar-sounding names. Here's how a CogniDev Playbook differs from a Claude Skill, a Claude Code plugin, and a generic agent — and why that difference is the entire reason regulated buyers will let it run.
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01
Agent
Generic AI primitive
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02
Claude Skill
Anthropic primitive
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03
Claude Code plugin
Anthropic primitive
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04
CogniDev Playbook
SI + AI · vetted recipe
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|---|---|---|---|---|
| A · Trust & repeatability | ||||
| Same input → same output | ✗ | ✗ | ✗ | ✓deterministic recipe |
| Hallucination guard | ✗none | —trusts the model | —trusts the model | ✓every reference resolves on live dep graph |
| Self-validation (verifier on every step) | ✗ | ✗ | ✗ | ✓typecheck · build · tests · lineage diff |
| Audit-grade evidence pack | ✗chat log only | ✗chat log only | ✗chat log only | ✓signed bundle per run |
| B · Cost & context economy | ||||
| Context piped to LLM | full conversation | SKILL.md dump + ad-hoc |
bundle context grows | ✓graph-narrowed slice only |
| Token cost per outcome | baseline $$$ | baseline $$$ | baseline $$$ | ✓20–40% of baseline · 60–80% cheaper |
| Hallucination risk as context grows | rises sharply | rises | rises | ✓bounded — only graph nodes sent |
| C · Structural intelligence | ||||
| Live AST + dependency graph | ✗ | ✗ | ✗ | ✓Tree-sitter + language-native parsers |
| Architectural patterns recognised | ✗ | ✗ | ✗ | ✓50+ — CQRS · hexagonal · DDD · event · layered |
| Source → target 1:1 traceability | ✗ | ✗ | ✗ | ✓every line lineaged to a structural decision |
| D · Knowledge & vetting | ||||
| Curated framework corpus (current versions) | ✗ | —partial (in SKILL.md) |
—partial | ✓Spring Boot 3 · FastAPI · React 19/Next 15 · .NET 10 · Terraform · K8s |
| Pre-built compliance packs | ✗ | ✗ | ✗ | ✓SOC 2 Type II · HIPAA · PCI-DSS · ISO 27001 · GDPR |
| Vetted by | nobody | skill author | plugin author | ✓library author + CogniDev review |
SKILL.md instructions get dumped wholesale. MCP responses, command output, hook traces, retrieval chunks — every tool call returns into the LLM window. Token spend grows with conversation length. Hallucination risk scales with irrelevant context. The model has no way to know what matters and what doesn't.
The dep graph is the filter. For every recipe step, only the exact subgraph the verifier will check goes to the LLM. The whole-repo context never enters the window. 60–80% fewer tokens · lower hallucination floor · same model class · audit-grade evidence on every run.
The unit of AI Engineering Ops.
A vetted, repeatable recipe for a real engineering outcome — grounded in your code, sandboxed at execution, evidenced at completion. Eight deterministic blocks under the hood; the LLM is one small bounded part of the pipeline, not the pipeline.
Outcomes, not tools.
A growing library of vetted, sandboxed, ship-ready outcomes — each one priced as a service, not a license. Browse by category, or auto-recommend from a repo scan.
COBOL → Java 24
Mainframe legacy to modern Spring Boot. Copybook decomposition, dependency-ordered conversion, source-to-target proof on every file.
VB.NET → .NET 10
Legacy ASMX/WCF Windows services to modern minimal APIs. Type-safe binding, async migration, DI throughout.
Perl/CGI → Python + FastAPI
CGI scripts and mod_perl to async Python services. Database access modernized, request handlers reshaped to FastAPI patterns.
SAS → Databricks + dbt
Statistical and ETL workloads from SAS 9.4 to lakehouse pipelines. Macros translated, datasteps reshaped to SQL/dbt models.
Java 8 → Java 24
Spring Boot 2 to 3, javax → jakarta, virtual threads, records, pattern matching. Build files, test runners, and CI updated together.
Monolith → Microservices
Identify bounded contexts on the dep graph. Carve services with strangler-fig sequencing. Generate API contracts & deployment manifests.
Adopt CQRS
Decompose reads/writes. Split commands & queries. Generate handlers, projections, and event flow — verified end-to-end.
REST → Event-driven (Kafka)
Convert sync REST integrations to event streams. Topic design, schema registry, idempotency keys, dead-letter queues.
SOC 2 evidence pack
Map controls to code. Generate access logs, change logs, encryption assertions, and reviewer-ready evidence bundles.
ISO 27001 control mapping
Annex A control traceability — link policies to implementation, surface gaps, generate the SOA & risk register.
HIPAA / PCI data flow audit
Trace PHI/PCI data through services. Tag boundaries, mask test data, generate the data-flow diagram and gap report.
Vibe-coded React → Production
Restructure AI-generated React chaos. Layer separation, state hygiene, error boundaries, accessibility, tests.
AI-generated Python → Production
Type the codebase, add layered architecture, dep hygiene with uv/poetry, pytest coverage, and runtime hardening.
Lovable / Bolt → SOC 2 ready
Audit Supabase RLS, lock down service keys, fix BOLA/IDOR, instrument logging — produce the auditor's evidence pack.
Cursor / Copilot architectural audit
Detect drift across AI-edited commits. Restore layer boundaries, surface dead branches, prove no behavioral regression.
AI hallucination sweep
Find imports, calls, and types that don't resolve on the real graph. Replace with what actually exists. Verify.
Replit Agent → Hardened deploy
Take a Replit-built MVP to a hardened cloud deploy. Containerize, secret-manage, RBAC, CI/CD with policy gates.
OWASP Top 10 sweep
Detect & remediate injection, auth flaws, insecure deserialization. Each finding tied to a structural location with a diff.
Dependency CVE remediation
Plan upgrades by impact and breaking-change risk on the dep graph. Patch, build, test, and prove no behavioral drift.
Secrets & AuthZ audit
Find leaked secrets, redundant scopes, missing authz boundaries. Generate a remediation plan ordered by blast radius.
Test coverage lift
Measure real coverage on the dep graph. Generate targeted tests for uncovered branches. Prove the lift with before/after.
JS → TypeScript migration
Type-by-type migration ordered by usage on the dep graph. Strict mode by default. Public APIs typed first.
For when the AI shipped, and prod broke.
Lovable, Bolt, Replit, Cursor, Copilot — they ship code fast. They don't understand your architecture, your compliance scope, or your blast radius. We do. We rescue what AI shipped and put audit-grade evidence behind every fix.
Hallucinated APIs & imports
Functions that don't exist. Imports that resolve at lint time but fail at runtime. Typed against guesses, not your actual modules.
Architectural drift
Layers melt. State leaks across boundaries. Each prompt-driven edit nudges the codebase further from any coherent design.
Secrets & data exposure
Service keys in client bundles. Row-level security never enabled. The Lovable, Moltbook & Bitwarden incidents — same root cause.
Authz & tenant leakage
BOLA / IDOR everywhere. Tenant boundaries that exist in the prompt but not in the queries. Easy to ship, ruinous to discover.
Untested side effects
Tests that mock everything that mattered. Migrations that pass locally and break at scale. No way to know what broke until prod tells you.
Compounding tech debt
Tech debt accumulates ~3× faster on vibe-coded apps. Each AI fix introduces three more. The compounding makes rewrite the cheaper option — until now.
Vibe Rescue playbooks
Lovable / Bolt → SOC 2 ready
Audit Supabase RLS, lock down service keys, fix BOLA/IDOR, instrument access logs & encryption — produce the evidence pack.
Cursor / Copilot architectural audit
Detect architectural drift across AI-edited commits. Restore layer boundaries, surface dead branches, prove no behavioral regression.
AI hallucination sweep
Find every imported symbol, function call, and type that doesn't resolve on the real graph. Replace, refactor, verify.
Replit Agent → Hardened deploy
Take a Replit-built MVP to a hardened cloud deploy. Containerize, secret-manage, set RBAC, add CI/CD with policy gates.
Vibe-coded React → Production
Layer separation, state hygiene, error boundaries, accessibility, real tests. Restructure the chaos behind the demo.
AI-generated Python → Production
Type the codebase, fix layered architecture, dep hygiene with uv/poetry, pytest coverage, runtime hardening.
AI-generated API → Sec-reviewed prod
OWASP API Top 10 sweep on AI-shipped routes. Fix authz, rate limits, injection vectors, idempotency. Ship with evidence.
Prompt-injection vulnerability scan
For apps that embed AI features. Detect untrusted-input → tool-call paths. Add policy gates, output validation, allowlists.
Why two intelligences beat one.
Pure AI guesses at your codebase. SI gives the model deterministic structure first — AI does what it's actually good at: synthesis.
Deterministic. Reproducible. Yours.
- Language-native parsers · typed AST
- Live dependency DAG
- Versioned rule library — every translation reviewable
- Zero hallucinated structure, control flow, or IaC
Bounded role. Contract-gated.
- Naming, comments, fixtures, long-tail bodies
- Every output passes a deterministic contract gate
- One shot — no retry loop on a failing contract
- See the engine in action →
Playbooks can't be exploited.
By design, not by hope. Every playbook runs inside guardrails — your security team and your auditors can verify it.
Sandboxed execution
Isolated runtime. No filesystem escape, no network exfiltration, no merges until you approve.
Vetted catalog only
Free, Basic, and Pro run only CogniDev-vetted playbooks. No prompt injection. No untrusted recipes.
Rate & scope limits
Per-tier quotas on runs, repo size, and parallel jobs. Predictable cost, predictable blast radius.
Full audit log
Who, when, which playbook, against what, with what evidence. Compliance-ready out of the box.
Approval gates
Multi-step playbooks pause at checkpoints. Reviewer approves or rolls back. No autonomous merging.
Private authoring
Enterprise customers author internal playbooks under review controls — your team, your library, same guardrails.
Your code never leaves your perimeter.
Cloud, private cloud, or fully on-prem — your choice, your control.
On-premise / air-gapped
The full platform behind your firewall. No external API calls. No data egress.
- Docker / Kubernetes
- Air-gapped supported
- Zero outbound traffic
Bring your own LLM
Self-hosted Llama / Mistral, private Azure OpenAI, AWS Bedrock — any API-compatible provider.
- No vendor lock-in
- Open or closed models
- Cost stays yours
Data isolation
Source, evidence, decisions, artifacts — all stay in your environment. Encrypted at rest and in transit.
- SOC 2 / ISO 27001 aligned
- RBAC + audit trail
- Tenant isolation
Three tiers. Per-user or team-licensed.
Standard and Pro are priced per user. Enterprise is a team license that includes the governance backend and central control-plane repo. Free trial on public / sample repos — no card required →
- All Standard playbooks — greenfield, quality, AI hallucination sweep, basic security
- Workbench, Standalone CLI, IDE extensions (Cursor, VS Code, JetBrains, Windsurf)
- Audit-grade evidence packs on every run
- BYO LLM (OpenAI, Anthropic, Google, Azure, on-device)
- Email support
- All Pro playbooks — migration, modernization, vibe-rescue, basic compliance (SOC 2 evidence)
- Higher run limits, parallel jobs, larger code corpora
- MCP Server, Claude Code plugin, Claude Skills distribution
- BYO LLM + on-prem option for the engine
- Priority support & reviewer-of-record on request
- All Enterprise playbooks — HIPAA, PCI, ISO 27001 with full audit; regulated migrations
- Cross-team governance dashboard — hosted by CogniDev or self-managed in your org
- Central control-plane Git repo — aggregates every playbook run across teams into one source of truth
- Cross-playbook orchestration & cross-agent communication — agents and playbooks coordinate across repos
- Action plan generation — organization-wide remediation plans from aggregated runs
- Private playbooks (custom rule libraries), SSO / SCIM / RBAC, air-gapped option
- Named CSM, SLA, dedicated review channel
Each playbook in the catalog is tier-marked — Standard, Pro, or Enterprise. The Enterprise team license includes the governance backend (hosted or self-managed) and the central control-plane repo that turns scattered playbook runs into a single dashboard and a unified action plan.
Get in touch.
Free trial, demo, or Enterprise call. We respond within 24 hours.
- Free trial — no card required.
- Enterprise — 50+ users, bulk discount.
- BYO LLM — self-hosted or any API-compatible provider.