The Fuzzy Problemby CODA · Clear Open Data AnalyticsNew technology looks finished long before it works.
That gap is the fuzzy problem. The tool is bought, the demo impressed, the AI talks fluently, and somehow the business is no better off. Nobody can tell whether it is working, roughly working, or confidently wrong. We specialise in closing that gap: twenty-five years of turning data into decisions, aimed at making new technology actually deliver. Don't take our word for it. You're talking to one.
The fuzzy problem
Every new technology arrives with the same promise and the same gap.
It gets installed. It demos beautifully. It produces fluent output. And it is nobody's job to check that the output matches the world. So it runs in a state that looks finished and is not. That is the fuzzy problem.
It is not a technology problem. It is the problem of getting technology to earn its keep inside a real business, with real data, real staff and real customers. It is the part every vendor leaves to you. It is the part we specialise in.
Looks done, isn't
The dashboard is live, the bot answers, the report lands. Nothing inside the build can tell you it quietly stopped working months ago.
Confident and wrong
Fluent output sounds the same whether it is right, roughly right, or made up. Confidence is not a signal, and most tools only give you confidence.
The demo that never became the day job
It worked in the pilot. Then it met your real data, your exceptions, your staff turnover and your Tuesday afternoons.
Nobody owns it
The vendor has moved on. IT says it belongs to the business. The business says it belongs to IT. It drifts until someone stops trusting it.
How we deal with it
The check always comes from outside the thing being checked.
Fluency cannot be trusted, so we never let a system grade its own homework. Four habits, applied to every build and every rescue.
Ground it
Every answer is tied to your approved data. If it cannot be traced to a source, it does not get said.
Check it against the world
A system checking itself certifies nothing. We verify outputs against the thing they are meant to change: the sale, the call, the enrolment, the number in the ledger.
Measure outcomes, not output
Not "the bot answered 4,000 questions" but "response time fell and enquiries converted". If the outcome did not move, it is not working yet.
Make it someone's job
Ownership, training and a feedback loop that keeps improving after we leave. Technology without an owner is a fuzzy problem waiting to happen.
From Toyota and HSBC to 10 Downing Street, government agencies and 25+ universities, CODA's founder has spent a career dissecting vast, messy data and turning it into decisions leaders can act on. That rigour is what makes new technology stick.
“CODA did a truly fantastic job of collating all of our data and turning it into something meaningful.”
Trusted across blue-chip, government & higher education
Where the fuzzy problem shows up. What we do about it.
Whether it is something we build or something you already bought, the job is the same: ground it, check it, measure it, and hand it to someone who owns it.
Implementation rescue
Already bought the tool and it isn't delivering? We find where it's fuzzy, fix it, and prove it's fixed against your real numbers.
Bespoke AI chatbots
Built from scratch and wired to your approved data, so they never make things up. Web, WhatsApp, Facebook & Messenger.
Customer-data Q&A engines
Plain-English answers over your live business data, every one traceable to its source.
Lead generation through AI
Conversational front-ends that capture and qualify leads, measured on the leads, not the chats. Like the one you're talking to.
WRAP — predictive analytics
Award-winning modelling for Withdrawal, Retention & Prediction. Proven at 70,000-student scale, now used to target any audience.
Data strategy & insight
Requirements discovery, KPI design, dashboards and forward planning. Messy data turned into decisions someone actually makes.
Fuzzy problems we have dealt with
Three engagements, each one starting from the gap between what the technology promised and what the business could rely on.
University of Bedfordshire
The fuzzy problem
Thousands of enquiries a year answered by hand, and an AI that could not be trusted not to invent an admissions rule.
What we did
A from-scratch Python chatbot and a customer-data Q&A engine, wired only to the university's approved data sources, with an evaluation loop and the staff trained to own it.
See the live chatadease.ai
The fuzzy problem
Small businesses paying for Google Ads they cannot read. Fluent AI advice is worthless unless every change is gated, logged and scored against real calls and sales.
What we did
A production SaaS where businesses run Google and Facebook Ads through a conversational assistant. Built end-to-end by CODA, with safety checks, standing rules and an outcome loop so the AI is judged on results, not on how confident it sounds.
Visit adease.aiAI quant engine
The fuzzy problem
An AI that recommends trades sounds impressive right up to the first time it is confidently wrong with money on the line.
What we did
An AI-driven quantitative trading and market-analysis system that ingests live market and news data, uses an LLM decision engine, and is held inside strict risk controls that learn from every outcome.
See the live platform