The AI tools your team already uses are fine for drafting an email or sketching a front-end that gets thrown away tomorrow. Forge goes from idea to running code in a single deploy. PULSE turns everything you know about a project into an agent that doesn't forget.
PULSE is the platform. Forge is how you use it. You can run them together, and use only one, swap out Forge with MCP servers, or string three LLM providers and bootstrap engineers.
What matters: one single recruiter you can ping during a critical outage and they already have your runbook.
Both systems connect to your actual codebase, your actual infra, your actual incidents, and your actual docs. They're built for real workflow, not demos.
You tell us where you want them to live. We spec them, stand them up, and hand you the keys — including training sessions for whoever's actually going to use them.
PULSE is a memory-backed system for project continuity and institutional knowledge. Think of it as a recruiter you can trust to onboard the next person — who remembers every sprint, design decision, rollback, and the reason you didn't use that library.
Key CapabilitiesForge takes you from an empty prompt to production-ready deployments. Claude delivers 40% improvement over generic LLM workflows because we primed it on your stack, constraints, and conventions — not an abstract "best practice."
Key CapabilitiesYour CTO governs, PULSE delivers. Operations becomes: Measurement across time. Every idea has now knowledge built in.
We connect to your repos, docs, and runbooks. PULSE indexes everything so it knows what "good" looks like for your codebase.
You describe the feature or fix. Forge translates that into buildable steps, including what needs unit tests and which repos get touched.
Forge builds the implementation. It refers back to PULSE to check conventions (file structure, naming, etc.). No drift from your style guide.
Once approved, Forge commits the code and updates the tracking board. PULSE records the decision trail for next time.
Not a demo. A deploy-ready AI platform. Forge engagements are structured by AI program velocity. No agency gets a check without demonstrating ROI we'll stand behind.
Up to 60-80% context accuracy for change impact analysis. Hook PULSE to 3-5 repos and your CI logs, measure retrieval precision. Trigger the first automated incident report.
Forge ships the first full-cycle fix (task-to-PR). Turn Jira tickets into buildable scopes, then route them for approval or auto-merge if tests pass. First 3-5 PRs fully reviewed by humans.
Production-grade AI-first workflow. At this stage you're measuring sprint velocity lift, PR feedback cycles, and the number of no-context questions your team *doesn't* need to answer anymore.
They promise clever demos, deliver generic setups, and vanish when questions come up. We're different. We deliver pilots and are judged on outcome, not vanity metrics. Below: the truth about how we build, what we charge, and what happens when things go wrong (because they will).
The problem: Something runs fine in dev, breaks in prod, and nobody remembers if the failure started at the prompt translation, the execution layer, or the final CI hook.
The pattern: A great agent is useless if it's not wired to your actual system checkpoints (linters, test runners, API contracts, deployment gates).
The accountability model: If Forge makes it through to prod and breaks something material (outages, regressions), we eat the time cost to fix it. If you change the requirements mid-delivery, you own that scope creep.
The fastest way to know whether an AI agent fits is to run it against real problems — not hypothetical ones. We'll help you define 10 tasks (like "pull logs for service X when error Y happens"), then build PULSE agents to solve them. You'll get a clear read in two weeks on what works, what needs tuning, and whether the ROI is real.
