AI readiness audit
Review existing repositories, tickets, docs, and operating rules before wider AI adoption.
HILLS Lab
Context infrastructure for software teams using AI.
We connect repositories, tickets, incidents, docs, and team decisions into a context layer that is current, verifiable, and permission-aware.
Focus
Reliable context before autonomy.
Context audits for existing engineering workflows
Repo, ticket, incident, and team memory
Permission-aware connectors for GitHub, Linear/Jira, Slack, Sentry, and docs
Evals that measure fewer wrong assumptions and safer code changes
Use cases
Where AI context pays off first.
Agent-safe knowledge base
Context from GitHub, Jira/Linear, Slack, and docs with source, scope, freshness, and ownership.
Coding assistant guardrails
Repo instructions, read/write boundaries, review rules, and traces that reduce wrong AI assumptions.
Incident and runbook context
Clean up runbooks, deploy rules, and incident knowledge that agents should not guess.
Recommendations
Proof from teams we have built with.
Povio, ReneVerse, and Tilt are HILLS Lab work. The other recommendations show the wider engineering track record behind HILLS Lab.
HILLS Lab engagement Open, professional, transparent in communication, and reliable on every agreed scope.
HILLS Lab engagement A top-tier engineer who made next-level impact across core Web2/Web3 services and real-time ad targeting infrastructure.
HILLS Lab engagement Multiple people have called out the quality and speed of Hrvoje's work across several projects.
Wider engineering track record
Ericsson Nikola Tesla Founder track record
Curious, proactive, and willing to take responsibility beyond his role early in his career.
Profico Founder track record
Someone we could truly rely on, not afraid to take responsibility and push new initiatives forward.
Rimac Automobili Founder track record
An exceptionally capable engineer whose code was built to last in a highly challenging hypercar telemetry and OTA domain.
Thesis
Models change. Context remains the work.
The team advantage is not which tool wins this quarter. It is whether the system around the model knows what is true, current, allowed, relevant, and source-backed.
Engagement model
A small pilot before a large promise.
Discovery
One conversation and a short review of tools, teams, repositories, and where context breaks today.
Context audit
We map sources, permissions, stale context, ownership, and risk for one concrete workflow.
Pilot
We harden one repository, team, or agent workflow so the result can be tested in practice.
Rollout plan
You get recommendations, priorities, and a clear decision on implementation or retainer work.
Six checks before the answer
A context layer is not an archive. It has to decide what an agent is allowed to use before the model writes, changes, or recommends anything.
Source
Which repo, ticket, incident, document, or decision supports the claim.
Freshness
Whether the information is still current or a newer record has replaced it.
Permission
Whether this context is allowed for this team, tool, actor, and task.
Risk
What could break production, leak data, or lead the agent to a wrong conclusion.
Change
What changed since the last run, deploy, incident, or recorded decision.
Trace
Whether a person can replay the path: sources, permissions, decision, and result.
PLAYGRND proof point
An AI-assisted product where context has to be verifiable.
We build PLAYGRND as a public record for amateur football: SSR web, private Go API, Postgres/Redis, WhatsApp magic links, claim/correction loops, and derived season aggregates. It is the same pattern we bring to teams: fast public reads, controlled writes, clear sources, and human confirmation before AI changes an official record.
For local software teams
For local software teams
Local companies rarely need a generic AI demo. They need AI workflows that respect existing repos, clients, documentation, permissions, and production risk without adding another layer of chaos.
Notes
Working notes from practice.
Short notes on failure modes, context rules, memory diffs, and evals for teams bringing AI into daily software work.