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Enterprise Execution

Controlled autonomous execution
for engineering teams.

Move from manual development and fragmented AI assistance to controlled autonomous execution — with verification, policy enforcement, and local operational control.

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Local-firstPolicy-enforcedVerification built inMemory active
21Files analyzed
96Semantic chunks
0Regressions
0.85Confidence
Evolution
From assisted coding to controlled execution
Three stages of engineering AI maturity.
STAGE 1

Manual development

Engineers write, test, and verify everything. High quality but slow. Doesn't scale with codebase growth.

STAGE 2

AI-assisted (copilots)

Suggestions and snippets. No execution. No verification. No memory. Engineer still does all the work.

STAGE 3

Controlled execution (SQ2)

System plans, executes, verifies, and learns. Policy-enforced. Scope-limited. Stops on uncertainty. Engineer approves, doesn't do.
Replaces
Where SQ2.ai replaces engineering overhead

Refactoring

Weeks of manual effort
↓
Hours. Scoped. Verified. Zero regressions.

Test coverage

Manual test writing
↓
Autonomous generation + validation.

Onboarding

Days of context gathering
↓
Immediate. Indexed. Searchable. Mapped.

Regressions

Debugging loops
↓
Validated correction. Self-healing loop.
Control
Autonomy without loss of control
Every action is policy-checked, scope-limited, and verified. The system stops on uncertainty — it does not guess.
01

Policy enforcement

47 rules. Blocked patterns. File boundaries. Dangerous command rejection. No exceptions.

02

Structured planning

JSON plans with scope, risk, verification checkpoints. Memory-informed. Human-approved.

03

Verification gates

Syntax. Diff coherence. Test generation. Self-healing. Every change validated before and after.

04

Reversible workflow

Step-by-step execution. Pause, inspect, resume, abort. No all-or-nothing commits.

05

Failure awareness

Stops on uncertainty, unexpected file spread, or policy violation. Logs everything for audit.

06

Critic review

Multi-agent quality gate. Correctness, completeness, and regression risk assessed post-execution.
Security
Built for controlled environments
  • ✓Runs entirely on your infrastructure
  • ✓No code leaves your environment
  • ✓No external API dependencies during execution
  • ✓No telemetry or data collection
  • ✓Air-gap compatible deployment
  • ✓Models run locally via Ollama
  • ✓All state under ~/.sq2 — inspectable, deletable
  • ✓Write scope limited to project root
Security posture
✓ data_egress: NONE
✓ external_calls: BLOCKED
✓ telemetry: DISABLED
✓ model_host: LOCAL
✓ code_access: READ_ONLY*
✓ write_scope: PROJECT_ROOT
✓ execution_journal: ENABLED
*writes require policy approval + verification
Architecture
Five execution layers
01

Understanding

Semantic index. Function-level chunking. Dependency mapping. Concept search.

02

Planning

Structured plans. Scope limits. Risk flags. Stop conditions. Memory-informed.

03

Execution

Policy-enforced. Step-by-step. Scope-limited. Stops on drift or uncertainty.

04

Verification

Syntax. Coherence. Tests. Self-healing loop. Every change validated.

05

Memory

Scored patterns. Time decay. Anti-patterns. Learns from every task.
Deployment
Deploy where control matters

Developer workstation

Single machine. GPU accelerated. CLI + local dashboard. Full autonomy in minutes.

Private cloud / team server

Shared instance. Centralized memory and journal. Multi-user support.

Restricted environments

Air-gapped. No internet after setup. Full offline operation with local models.
Engagement

Start with a real repository

2-week engagement on your actual codebase. We deploy, you observe. Measurable output against your manual baseline.

01 Deploy SQ2.ai on your infrastructure
02 Index your repository
03 Execute 3–5 real engineering tasks
04 Review verification quality and execution journal
05 Evaluate results against manual baseline
START ENGAGEMENTVIEW ENGAGEMENT DETAILS

Not a copilot.
A controlled execution system.

SQ2.ai doesn't suggest code. It carries engineering work forward — with structure, verification, and accountability that scales.
FAQ
Common questions
Does SQ2.ai require cloud connectivity?
No. SQ2.ai runs entirely locally. Models execute via Ollama on your hardware. No data leaves your environment.
What models does it use?
Any Ollama-compatible model. Default: Qwen 2.5 14B (primary) with Llama 3 as fallback. GPU acceleration recommended.
Can it modify production code without approval?
No. All writes require policy approval. Execution is step-by-step with pause/resume/abort controls. No automatic git commits.
How does it handle errors?
Self-healing loop: if tests fail, the system analyzes the error, generates a fix, applies it, and re-tests. Max 3 retries, then stops.
What's the engagement process?
We deploy on your infrastructure, index your repo, execute real tasks, and measure output quality against your manual baseline. 2 weeks, no commitment.
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