Grounded Brain.
Deterministic Hands.
Codelign indexes your enterprise systems into a graph of facts, reasons over them, and ships the change — plan, implement, deploy. Every verdict comes from your systems, never a model's guess. And it runs entirely on hardware you own.
A copilot that guesses has no business in your systems.
Generic AI assistants don't know your stack. They invent field names, miss the logic that actually fires, and can't show their work when an auditor asks. In a regulated enterprise, a confident wrong answer is worse than no answer at all.
Same question. Two behaviours.
"That's controlled by Account.Tier__c, updated by the Tier Assignment flow."
Confident, fluent — and invented. Neither the field nor the flow exists in this org. Nothing to click, nothing to audit.
Tiering is written by Segment_Rank__c, set in the Account Scoring record-triggered flow, gated by the Enterprise Tier validation rule.
Every claim links to a fact in the index. No evidence, no answer — it flags the gap instead of filling it.
Three rules everything else is built on.
Facts, not weights.
Your systems' truth is indexed and queried at runtime — data models, code, automations, rules. Nothing about your stack is ever memorized into a model.
source of truth = your systemsModels propose, code disposes.
A cloud brain may suggest a plan. The local runtime validates it against a fixed tool registry, executes it, and owns every verdict. Off-list steps are rejected.
deterministic verdictsUnknown beats wrong.
Missing evidence is a flag, not a blank to fill. The system tells you what it can't prove instead of guessing — the one behavior enterprises actually need.
no confident fabricationGround → Reason → Act. One governed pipeline.
Facts in, validated plan in the middle, deterministic execution out. The reasoning is rented; the decisions are yours.
Index the system.
Build a queryable graph of your metadata plus exact, step-by-step process traces for any event.
- data models & fields
- business logic & rules
- code, triggers & automations
- end-to-end event traces
Plan under contract.
A brain proposes {tool, args, why}. The local side validates it against the registry before a single step runs.
- local model by default
- Claude · Codex · Gemini on demand
- off-list or bad-args → rejected
- privacy switch decides routing
Execute deterministically.
Narrow, verifiable operations — each one auditable, each one reversible in review.
- localize a story to its seam
- deploy w/ live progress + auto-repair
- Playwright regression authoring
- cited answers, never opinions
Your data never
leaves the building.
Codelign ships as an on-prem appliance on AMD Ryzen AI. The default brain is a local model — light, fast, private. Cloud reasoning is called only when you explicitly allow it and the data isn't sensitive.
The privacy switch isn't a policy you trust. It's code in the path — local-only until you say otherwise.
Built for the teams that can't send data out.
The architecture is the security story. Nothing about your systems is memorized into a model, and nothing sensitive leaves your perimeter unless you deliberately allow it.
Data residency
Runs on your hardware, in your datacenter. Indexed metadata and any records stay inside your perimeter — full stop.
Local-first by default
The privacy switch is code in the request path. Cloud brains are opt-in and gated per request — never the default route.
No training on your data
Your systems are indexed as queryable facts, never used to train or fine-tune any model. Your truth stays out of model weights.
Auditable by construction
Every answer cites its evidence; every action is a narrow, logged, reversible operation. When an auditor asks, you can show the trace.
Least-privilege access
Scoped connectors and read-first indexing. Deploys run through your existing release tooling and its approvals — not around them.
Deterministic guardrails
Off-list or malformed model plans are rejected before execution. Models can't do what the tool registry doesn't explicitly allow.
The whole delivery loop, grounded.
Grounded Q&A
Answers about your systems with the exact components as evidence. Every claim links back to a fact in the index.
Process discovery
Entity-driven business-process maps across your stack, with exact step-by-step traces for any event.
Story placement
Localize a change request to the precise seam in your codebase where it belongs, before anyone writes a line.
Deploy engine
Your existing release tooling — Copado, Gearset, Azure, CLIs — through one interface, with live progress and auto-repair.
Regression authoring
Playwright scenarios authored and executed against your UI, with a preflight probe before any run.
Scheduled refresh
Daily repo sync keeps the index current — baseline, approved overlays, reapply and reindex, hands-off.
One platform. Many systems.
Codelign runs deep on Salesforce today — full metadata indexing, process traces, plan-to-deploy. The architecture is deliberately system-agnostic: the grounding, the governed contract and the privacy switch stay the same. To reach the next platform, only the connector changes.
Codelign Labs
We think the reason AI hasn't landed in serious enterprise systems isn't capability. It's trust.
Codelign Labs builds grounded AI for enterprise delivery — software that reasons over your systems the way a careful engineer would: from the facts, showing its work, and stopping when it isn't sure. Our answer to the trust problem is an architecture where models reason but never decide, and where your data never has to leave the building.
Two principles run through everything we ship.
The things enterprise teams ask first.
Where does Codelign run?
On-premises, on an AMD Ryzen AI appliance inside your own environment. A local model handles reasoning by default — nothing depends on an outside service to answer a question about your systems.
Does our data or metadata leave the network?
Not by default. The privacy switch is local-only until you explicitly allow a cloud brain, and even then only for requests you've marked non-sensitive. Live customer records are never bulk-stored or sent.
Which systems does it support today?
Salesforce, deeply — full metadata indexing, process traces, and plan-to-deploy workflows. The architecture is deliberately system-agnostic; additional connectors are on the roadmap.
How is this different from a generic AI copilot?
Copilots reason from model weights and will confidently guess a field or a flow that doesn't exist. Codelign grounds every answer in your indexed facts, cites the evidence, and refuses to answer when the evidence isn't there.
Does it replace our deployment tooling?
No. It drives your existing release tooling — Copado, Gearset, Azure, CLIs — through one interface, with live progress and auto-repair. Your approvals and pipelines stay in place.
How do we get started?
Book a demo. We'll index a sample system, trace a real event end to end, and plan a change — live, on the appliance. Reach us at support@codelignlabs.com.
See Codelign reason over a system that looks like yours.
A 30-minute walkthrough on your terms. We'll index a sample system, trace a real event end to end, and plan a change — live, on the appliance.
support@codelignlabs.com · Codelign Labs Pvt Ltd