We rescue AI investments
that aren't delivering.
We turn stalled AI initiatives into production systems people actually use through software engineering, AI architecture, and workflow design that surface the value already inside your AI investment.
Three questions that tend to make the room go quiet.
Can we tell every time the model makes a mistake?
Would last month's AI outputs survive an audit?
Can we tie AI adoption to business outcomes?
You have already adopted the obvious measures.
Tightened the prompts. Added a second tool to monitor the first. Set up an internal task force, pressured the vendor, ran one more pilot. None of it was a bad idea, but it didn't settle your doubts, because the problem is structural: the system has no way of knowing when it is wrong. Every vendor ships these tools that way. Your rollout didn't solve for the gap.
A structured four-stage engagement to diagnose, stabilize, and scale your AI systems.
Diagnose
We follow one unit of work end to end.
One statement, one quote, one alert, traced from input to consequence, asking where correctness gets decided, by whom, and what happens when it is wrong. This takes days, and it usually explains more than the six months of internal debate that preceded it.
Harness
We build the check that lives outside the AI.
Every step that has a right answer gets verified by something with none of the model's blind spots: the totals a document prints on itself, a ledger that already closed, the record of what the technician actually found. From then on, wrong answers stop looking identical to right ones.
Calibrate
We run it live and tune who sees what.
The harness clears the routine cases and routes the doubtful ones to your people, each case arriving with its failure point marked. The thresholds — what runs on its own, what waits for a human — are set with your executives and adjusted as live results come in, because that line is a business decision, not a technical one.
Demonstrate
You get a number the CFO can use.
The share of work that now runs without review, the hours returned to the team, the errors caught before they shipped. Measured on your operation, reported in your terms, and owned by your people when we step back.
The screen looks the same whether the number is right or wrong.
AI often fails quietly and confidently. A model that misreads a statement, misprices a quote, or misses a failing machine produces the same confident tone as when it gets everything right. Most deployments have no way to tell the difference, which means the teams running them find out from a customer, at the month-end close, or not at all.
So everything we deploy ships inside a harness: a check that lives outside the AI and cannot fail the way the AI fails. The totals a statement prints on itself. A ledger that has already closed. The work order recording what the technician actually found on site. The AI does the reading, and a deterministic process does the checking. The small share of cases that fail the check reach a person who can see exactly which check failed; half the review done before it starts.
When a project starts with us already involved, the harness is designed in from the beginning. When a system is already live and drifting, the work usually starts by finding where it is wrong and building the check that would have told you.
An AI’s answer, and three ways to treat it. Why only the third one can catch an error the model is confident about.
We call our approach The Lami Triangulation, after Lami's theorem in statics: three independent forces are in equilibrium only when the system is consistent — and when it is not, the size and direction of the imbalance point to what is wrong.
Three instances that demand the same rigour.
We run every engagement with the same proprietary methodology: diagnose, harness, calibrate, demonstrate. What differs is where you are when you reach out to us: an investment that needs to show value, an account that needs to be salvaged, or a new initiative that should never need rescuing.
Your AI is live. The value isn't.
For the executive who owns an AI spend that is under scrutiny. We diagnose where the initiative actually stands in days rather than months, build the verification and adoption layer it shipped without, and stay embedded until the value shows up in a number finance accepts.
- A diagnosis in days: who decides a right answer, and what happens after a wrong one
- The harness: checks that live outside the AI and catch what it cannot
- A value number measured on your operation, reported in your terms
Keep your biggest customers from churning.
For the founder or delivery leader watching a flagship enterprise deployment slip. We embed as your senior delivery team: technical recovery, forward-deployed engineering, and executive stakeholder management, until the account is back on track.
- Senior forward-deployed engineers, embedded in the account
- Someone senior in the steering committee, on your side of the table
- Playbooks and patterns your own team keeps when we leave
The least expensive rescue is the one you never need.
For teams moving upmarket to enterprise clients for the first time. We design the verification, the calibration loop, and the human handover in from the first architecture session, so the system earns trust from its first week in production rather than losing it slowly over months.
- The harness designed into the architecture from the first session
- Thresholds set with the executives who own the risk
- Proven on live work before it scales
If you are the one selling the AI.
Your product demos well and the contract is signed. But the enterprise deployment is consuming more senior engineering than your bench can spare, and the reference account your next raise depends on has started to feel fragile. We step in as your delivery arm: engineers who have sat on the client side of the table, embedded with your team or fronting it, until the deployment holds on its own.
Talk to us about deliveryWhere the numbers moved.
Two kinds of client profiles, one problem: making AI work.
Not every AI engagement is ours to take. The ones that we engage share a pattern: a real investment that matters enough to be worth rescuing, and consequential decisions with low forgiveness for wrong answers. The buyers come from both parties, an enterprise defending a spend, or an AI product company defending an account.
Where our pattern recognition runs deepest
Who calls
What usually triggers an engagement
- AI has been bought or piloted, and the CFO is asking what it returned
- An AI system is live, but nobody can say when its output is wrong
- A flagship enterprise deployment is slipping, and engineering is tied up doing delivery firefighting instead of building the product
- A tool was bought to win back time, and the team still works around it
- The team has quietly gone back to the old process, and nobody has told the sponsor yet
If the situation sounds familiar, the diagnostic conversation is a good place to start.
What people usually ask before the first conversation.
What does Raining Code do?
We are the company you call when an AI initiative is in trouble. For the enterprise executive defending a spend, we diagnose where the system actually stands, build the verification and adoption layer it shipped without, and stay embedded until there is a number the CFO accepts. For the AI product company defending an account, we act as the delivery arm. Senior-partner-led, across Europe and North America, independent of any tool.
Our AI pilot stalled. Can it be rescued?
Usually, yes — and the diagnosis is faster than most teams expect. We trace one unit of work end to end and ask where correctness gets decided, by whom, and what happens when the AI is wrong. In most stalled projects the answer is nowhere, no one, and nothing, which explains both the stall and the fix. That diagnosis takes days, not months, and it tells you whether the investment is recoverable before you spend anything more on it.
What is an AI harness, and why does it matter for sales and pricing?
A harness is the structure around an AI system that makes its output trustworthy enough to act on — a check that lives outside the model and cannot fail the way it fails. It is why a quote, a price, or a churn signal from AI can be relied on rather than second-guessed. The reliable commercial gains — faster quote turnaround, sharper RFP response, churn caught while you can still act — only hold once the verification is there. Pointed at no particular process, AI just scales the existing mess faster.
Should we buy another AI tool to fix the one we have?
Almost never as a first move. A tool that is not delivering usually lacks the verification, calibration, and workflow around it, and a second tool inherits the same gap. We are independent of any vendor, so when a purchase is genuinely the right answer we will say so — but the recoverable value usually sits inside the investment you already made. We map buy, build, or leave alone before anyone spends.
We sell an AI product and our enterprise deployment is struggling. Can you help?
Yes. Enterprise deployments consume senior field engineering that most AI companies cannot staff deeply enough, and a wobbling flagship account puts the next raise at risk. We act as your delivery arm: senior engineers who have sat on the client side of the table, working under your flag or alongside it, until the deployment holds on its own.
Who rescues failing AI projects in Europe and North America?
Raining Code does — senior-partner-led, with Rohit Chikballapur in Basel and Adi Sankaran in Houston. We work with enterprises whose AI spend is under scrutiny, and with AI product companies whose deployments need senior delivery. The people doing the work have built, shipped, and run production AI themselves.
The practice we would have hired when ours stalled.
Operators who have built, shipped, and run production AI inside operating companies and who now spend their time getting other people's AI initiatives back on track. A senior partner in your market, the full bench behind them.
Rohit Chikballapur leads delivery and the rescue discipline. Seven years building and deploying industrial AI at Facterra across European & North American clients: the pricing, service, and after-sales experience that decides whether an AI investment compounds or quietly fails after the rollout.
Adi Sankaran leads the delivery-arm work. He ran product for an AI-native field service platform at Zinier, shipped to enterprise operators worldwide, and has lived the exact failure mode the rescue motion exists for: a platform that worked, deployed into a customer where everything around it did not.
Sushobhan Mukherjee leads the brand and demand practice. Thirty years in brand strategy: Digital Design and Strategy at Infosys, a Grand Effie and a Jay Chiat Award, a media company he co-founded acquired by the Financial Times. His benchmark is whether the work converts, including whether AI assistants name you when buyers ask.
Meet the practice