AI Consultancy · For technical teams
For engineering teams, CTOs, and enterprises who are past the question of whether to use AI and into the harder part: making it reliable, affordable, and safe to run at scale.
If you're not yet sure where AI fits your business, you want the AI Opportunity Audit instead.
Your AI feature works in demos and fails unpredictably in production
The demo ran on clean samples. Production data is a different shape, and nobody knew until the numbers dropped.
You're stuck on an existing system you can't debug
It's live, it's expensive, and every attempt to fix it makes it worse. You need someone who has been inside these systems before, not a fresh pair of eyes.
Token costs are growing faster than usage and nobody can explain why
Spend is up, the dashboard says usage is flat, and the two numbers stopped agreeing a while ago.
Your team ships prompts with no evaluation harness behind them
Changes get approved because they sound better, not because anything measured them.
You're carrying an AI system someone built and left
The engineer who built it moved on. The system still runs. Nobody fully knows how, or what it costs.
A board or regulator is asking questions your architecture can't answer
Where is the data? How is the output controlled? What happens when it's wrong? The questions are reasonable and the answers aren't ready.
You need a senior second opinion before committing to an expensive direction
The decision is real, the money is real, and the internal team is too close to it to be objective.
This page is not for you if you're at the “we should probably do something with AI” stage. That's a different, cheaper, faster conversation · start here.
What we take on
1–2 weeks · Written assessment
An independent read of your AI system: model choices, orchestration, retrieval design, evaluation coverage, failure handling, cost structure, and where it will break under load.
You get
A written assessment with prioritised findings, severity-rated, plus a remediation sequence. Delivered to your engineering team, not your board.
Typical trigger
Something is wrong and the team is too close to it to see why.
1 week · Written report + reworked assets
Where most production AI quality and cost problems actually live. We review prompt architecture, context construction, retrieval relevance, token economics, and evaluation coverage.
You get
A cost and quality breakdown, reworked prompts and context strategy, and an evaluation harness so future changes are measurable rather than vibes.
Typical trigger
Quality is inconsistent, or spend is climbing without a matching increase in usage.
Ongoing · Retained
A senior AI engineer on call for your team. Design reviews, build-versus-buy decisions, model selection, escalation on hard problems, and sanity checks before expensive commitments.
You get
Scheduled sessions plus async availability, scoped monthly.
Typical trigger
A capable team with no one who has shipped AI at scale before.
Ongoing · Retained
An AI leader on your team without the full-time cost. Owns the technical roadmap, runs evaluations and reviews, sets the standards your team works to, and stays accountable for the results.
You get
Part-time engineering leadership with a defined scope, reviewed monthly.
Typical trigger
Your team is technical but there's nobody accountable for how the AI work fits together.
2–4 weeks · Strategy document + technical roadmap
For teams building AI products rather than internal systems. Capability positioning, defensibility, cost modelling at scale, and the roadmap that connects them.
You get
A strategy document with a technical roadmap attached, because AI product strategy that ignores unit economics isn't strategy.
Typical trigger
You're building something to sell, and the margins depend on architectural decisions you're making right now.
Not sure which fits? We'll tell you honestly, including if you don't need us.
Why us
Grapine ships five AI products of its own: context compression, competitive intelligence, outreach automation, engagement tooling. Every one exists because we hit the problem in our own work and needed the fix.
That's the relevant credential here. Reviewing an AI architecture well requires having owned one after launch, through the cost surprises, the silent quality drift, and the failure modes that only appear at volume.
25 years
in software, deep in AI
1
granted patent
Microsoft + Accenture
production deployments
5+
products shipped
How this differs
Service
You are
You get
A technical team already shipping AI
Senior review, remediation, and direction
Let's talk
Send the architecture, the cost curve, the failure logs, or just the description. We'll tell you what we think and whether we're the right people for it.
Prefer email? [email protected], we respond within 24 hours.