Agent-Ready Context & Orchestration
High-fidelity prompts and codebase intelligence for the AI-augmented developer.
The Context Gap
Why Coding Agents Hallucinate
Most coding agents fail because they lack the "Why" behind the "What." They can see the file you're working on, but they don't know the architectural decisions made six months ago, the PRD requirements written yesterday, or the related logic hidden in a different repository.
The result?
Developers spend more time fixing AI code than writing their own — turning a productivity tool into a productivity tax.
# What your agent gets today
"Write a login page."
→ Generic, non-functional, breaks existing auth patterns.
# What your agent gets with Signals
"Write a login page using our Auth0 wrapper,
following styling in /styles/theme.css,
and handling the 2FA edge case
found in last week's Retro."
→ Production-ready code on the first attempt.
Signals Context Engine
PRD + Git History → Master Prompt
Input — Raw PRD
billing-toggle-v2.md
- Add billing toggle to settings
- Talk to finance team re: legacy
- Must work on mobile somehow??
- Don't break existing users lol
- Sarah said check old migration
...
Unstructured, ambiguous, incomplete.
Signals Processing
Syncing with Git history,
past retros & architecture docs
Output — Master Prompt
context-bundle.prompt
System Goal:
"Implement the new billing toggle."
Reference Files:
lib/billing.ts
api/v1/user.go
Constraints:
"Ensure compatibility with
the 2025 DB Migration."
⚠ Fragile: billing.ts has
high bug rate (last 90d)
Deep-Context Prompt Engineering
Signals Prepares the Battlefield
We don't just talk to AI — we give it the same organisational context your senior engineers spent years learning.
PRD-to-Prompt Mapping
We automatically break down your Product Requirement Documents into technical instructions that coding agents actually understand — no more "lost in translation" hallucinations.
Historical Decision Awareness
We inject context from past commits and retrospectives into the prompt so the AI doesn't repeat old mistakes or break legacy patterns baked in years ago.
Cross-Repo Intelligence
If a task requires changes in both the frontend and the API, Signals identifies the dependencies and provides a unified context map — no more silos.
One-Click Copy / Paste
Get a perfectly structured prompt ready for any LLM or coding agent — Cursor, Claude, Copilot, or your own internal toolchain.
Feature Breakdown
How Signals Makes Every Agent Smarter
Org-Context Injection
We index your entire codebase and documentation to identify "How we build things here."
AI code that follows your specific linting, naming, and architectural standards.
Agent Action Plans
Instead of a single ticket, we generate a step-by-step technical plan for the agent.
Reduced "looping" where agents get stuck or go in circles trying to solve under-specified tasks.
Risk-Aware Prompting
If the code repo shows a fragile area — high bug rate, recent regressions — we add a warning to the prompt context.
Safer AI deployments and fewer regressions in critical, battle-scarred parts of the codebase.
The Business Case
"Stop paying for AI tools that guess. Start giving them the 'Signals' they need to be accurate. We reduce the 'AI Fix-it' tax by providing agents with the same organisational context your senior engineers have spent years learning."
10×
Agent accuracy improvement
↓ 60%
Reduction in AI rework time
↓ 40%
Fewer AI-introduced regressions