Decifer Markets
Market intelligence
Decifer Markets turns market noise into a plain-English read on what is moving, why it matters and what to watch, across stocks, themes and catalysts.
Open MarketsAI implementation, Dubai
Turn AI investment into operating results.
Decifer takes an AI project the whole way into daily use: the business case, the workflow redesign, the build, the integration, the rollout, and the measurement afterwards. One team for all of it. Every system runs on your accounts, with a log your team can read.
Engagements start with a two-week assessment at a fixed fee, credited in full against any build.
- 3
- live systems you can open and use before you hire us
- 719
- documents from thirty years of trading, searchable in weeks
- 16
- data sources feeding one set of numbers everyone reads
Every figure on this site names its source and the date it was last checked.
The AI implementation gap
Published research, cited95%
of enterprise GenAI pilots produce no measurable P&L return.
MIT, The GenAI Divide: State of AI in Business, 2025
30%
of GenAI projects would be abandoned after proof of concept by the end of 2025, Gartner predicted, blaming poor data, weak controls, rising costs or unclear business value.
Gartner, July 2024
84% / 31%
In the GCC, most companies now use AI in at least one function. Fewer than a third have scaled it across the business.
McKinsey, The State of AI in GCC Countries, 2025
11%
of GCC organisations qualify as value realisers, able to attribute at least 5% of earnings to AI.
McKinsey, same study
The gap is in implementation, not technology. Everything below is how we close it.
What separates the projects that ship
Six failure points, and what we do about each.
None of these are model problems, so a better model does not fix them. Every Decifer engagement is structured against this list, starting with the baseline.
The business case is weak
The pilot was chosen because the technology looked capable, not because anyone costed the process it replaces. When budgets are reviewed, there is no evidence to defend the spend.
We cost the process before any technology is chosen, so the spend can be defended at a budget review.
The old process was carried forward
The workflow was designed around people, email and spreadsheets, and a model was bolted on top. The organisation gains another tool while the old work remains.
We redesign the workflow first, then automate the version worth keeping.
Data and integration arrived late
The demo ran on controlled inputs. Production needs the CRM, the inbox, the documents, the permissions and the history, and that is where scope and cost change.
The real systems go in early: the CRM, the inbox, the documents, the permissions and the history.
Exceptions were never designed
Real processes contain missing information and unusual cases. Nobody decided what the system handles, what a person reviews, and how a failed action is recovered.
The exception path is designed with you: what the system handles, what a person reviews, and how a failed action is recovered.
Nobody could trust the output
One invented figure in front of a customer, and the team quietly goes back to the old way.
Every figure is worked out in code, and the system will not publish one it did not calculate.
Nobody took a baseline
Time saved, cost reduced and response time all need a starting point. Without one, ROI becomes an opinion, and the project dies at budget time.
The baseline is the first deliverable, so the result can be measured against it later.
The architecture
Six failure points, one missing drawing.
Projects stall those six ways because nobody drew the system first. This is the drawing: one request enters, moves through four steps, and the answer goes back. Nothing here is specific to us. It is the shape any AI system in a business has to have to be trusted with real work.
- Four steps, every request: assemble, ask, check, act.
- Control runs across all four, not at the end.
- More than one model, with routing and fallback.
- It reads and writes the systems you already run.
Built by Decifer
Evidence, not an offerWe run our own systems in production.
Before we sell a method, we run it. Operating real products builds a discipline that demonstrations do not: real users, real data, model failures, infrastructure cost, monitoring and support. Decifer operates three public products built the same way we build for clients. They are not what this site sells. They are how we know the method holds.
5
months running every day with nobody operating it
Source: decifer-trading git history, first commit 2026-03-25. Verified 2026-08-22.
30+
jobs that run overnight so nobody has to remember to start them
Source: crontab, vercel.json and launchd files per repo. Verified 2026-08-22.
25+
business systems already connected: CRM, email, ads, payments
Source: integration clients per repo, deduplicated. Verified 2026-08-22.
9,000+
automatic checks that run before any change reaches a user
Source: decifer-trading tests/, counted 2026-08-22. Verified 2026-08-22.
Every figure above is listed with its source and the date it was last checked on how we count.
- Live
BetaDecifer Learning
Learning intelligence
Decifer Learning is a guided companion for the UK National Curriculum. Children learn, practise and quiz through each topic while parents see real progress.
Open Learning
LiveDecifer Marketing
Marketing intelligence
Decifer Marketing turns campaign, channel and audience data into a plain-English read on what is working, why, and what to do next.
Open Marketing
The investing system trades a broker paper account. It has never submitted a live order and is not a real-money track record. We say this everywhere it is mentioned.
What we do
How engagements runAI agent development
Two to eight weeks, then a retainer
Remove manual work from processes that can be measured.
We redesign the process, then build the system around it: agents scoped to one job, with limits you can see and a log you can check.
Scoped in writing
Human review where a mistake is expensive
Read the service
Data and reporting automation
One to eight weeks, fixed fee
Make business information usable.
Your data lands in one place you can query, including the data trapped in documents, and the reports assemble themselves from figures computed in code.
Fixed fee
Raw data exported to you
Read the service
AI product development
Six to twelve weeks, fixed fee
From nothing to live in weeks, and you keep the repository.
A complete product: website, database, logins, payments, email and analytics, built in weeks and handed over with the code.
Fixed fee
The repository transfers at handover
Read the service
AI consulting and assessment
Two weeks, fixed fee
Find the opportunities worth funding, and the ones that are not.
A fixed-scope opportunity assessment that maps where time actually goes, costs the current process as a baseline, and tells you plainly what to automate first and what to leave alone.
Fixed fee
Credited in full against any build
Read the service
These are the four shapes an engagement takes. The twenty workflows we have already built sit inside them.
Discuss a business processThe rule we build by
What makes a system safe to run the business on.
Every part of a system gets a defined job, decided by one question: does this step need judgement, or does it need to be right? Where plain code does the job, we use plain code: it costs less to run every month and it cannot invent anything. We have made that swap in our own systems 5 times.
The rule, step by step
In
Inputs that can be checked
Your documents, your records, live reads from the systems you already run. Everything the system may answer from, and nothing it may not.
Calculated
Code works out the numbers
Prices, scores, totals, checks, business rules. Worked out the same way every time, and the only place a figure can come from.
Written up
AI writes the words
Reading documents, sorting, summarising, drafting the reply. It may explain a figure it was handed. It may never produce one.
Owned
A person keeps the decision
Anything expensive has a named owner, an approval step, and a route out when the information is not there.
The line is held by a check, not by a promise. If a model writes a figure the code did not calculate, the change does not ship. That check runs across all four stages, which is the only reason the rule above survives contact with a deadline.
Numbers your customers see cannot be invented
Prices, scores, totals and business rules are calculated by ordinary software, the way your finance system calculates them. A model never decides a figure, and the system refuses to publish one the code did not produce.
AI does the reading and writing, not the arithmetic
Reading documents, sorting enquiries, summarising, drafting the reply: work where the input is messy and no fixed rule would cope. That is where a model earns its cost, and it is a smaller part of most jobs than vendors suggest.
Anything expensive has a named person on it
Approval, review and escalation are built into the workflow, so a higher-risk decision reaches the person who owns it. Nobody has to remember to check.
The system says when it does not know
When the information is missing or contradictory, the case goes to a person, with the gap named. Nobody spends a morning unpicking a confident answer that was wrong.
Your team can see what happened, without calling us
A plain record of what came in, what the system did and what followed. Written to be read by the people who run the process, not by a developer.
You own it, and you can leave
Everything runs on your accounts and transfers to you with documentation, tests and a runbook. Staying with us is a decision you make each year, not a position you are stuck in.
One team, business case to production
Six steps, one owner.
This path is usually split between a consultancy, a development shop and an internal IT team, and the seams between them are where projects die. Decifer carries all six.
01
Find the value
We examine the work as it runs today: where time disappears, where people re-key information between systems, and which processes get more expensive as volume grows. We cost the opportunity before choosing any technology. Output: a business case, a current-state baseline, prioritised use cases, and the order to build them in.
02
Redesign the workflow
AI changes what software can handle, which usually means the process itself should change. We decide which steps need conventional code, which benefit from a model, which need human judgement, and where approval and escalation belong. Exceptions are designed here, not discovered later.
03
Build the system
Agents, document reading, workflow automation, reporting, internal applications and the connections between your systems. The design follows the requirement, and anything that has to be right is calculated by tested code rather than left to a model.
04
Connect the business
Useful systems work with what already exists: the CRM, email, documents, databases and third-party APIs. We wire the workflow to the information and actions it needs, with access controls and an audit trail.
05
Put it into operation
Production raises questions a demo never meets: who has access, what happens when information is missing, how a wrong action is reversed, who receives an exception, and who owns the system internally. We resolve them before the system becomes part of daily work, and we train the people whose work changes.
06
Measure the result
The implementation is scored against the baseline from step one: processing time, employee hours, response time, cost, error rate, capacity or conversion, whichever the business case named. The business should be able to see what changed.
Processes we've taken to production
The full matrixOne system instead of scattered paperwork and memory
Documents, spreadsheets and years of institutional memory, turned into one record that is searchable, correct, and does not live in one person's head.
719 order documents from 30 years of trading became one priced, queryable record.
Also fits:Insurance claims historyLegal matter filesBanking back-office records
Read the case
A number everyone in the business already trusts
One fact store, refreshed on a schedule, with every report built from those facts in code. A model may write the sentence around a figure; the figure itself always comes from the code.
Nine operating companies now read from one dashboard, refreshed nightly instead of assembled by hand each quarter.
Also fits:FP&A and board reportingMulti-brand or multi-subsidiary groupsPE portfolio company roll-ups
Read the case
An agent that completes the task, or hands it to a person
An agent scoped to one job, wired into the systems it needs, with a written definition of what it may do, what it hands to a person, and a log of every action it takes.
A concierge answered 40 out of 40 test questions correctly, partly by refusing the ones it could not verify. A second build cannot exceed its ad budget, because the limit is written into the code rather than into a prompt.
Also fits:Contact centres and support queuesSales development and qualificationClaims intake
Read the case
Knowing exactly where AI should not be used
We decide where AI is genuinely too risky, and remove it from that part of the system completely: no field to type a number into, no AI in that step at all, a person alerted before anything happens.
Zero client-facing AI in a clinical intake system; screening scored by arithmetic against published cutoffs.
Also fits:Healthcare and clinical operationsHR and people decisionsLegal advice
Read the case
Built to run with nobody watching it
Every engagement held to the bar of a public product: it monitors itself, tests itself, and arrives with a runbook and a proper handover.
Decifer Markets has run every day since March on a broker paper account, with every change checked automatically before it goes near a user.
Also fits:Any implementation where the real question is whether it survives without supervision
Read the case
Don't see your industry above? That's exactly what the first call is for.
Discuss a business process10/20/70
Algorithms / technology and data / people and process. Source: Boston Consulting Group.
Implementation changes how people work
BCG attributes roughly 10% of AI success to algorithms, 20% to technology and data, and 70% to people and process. That matches what happens in practice. A redesigned workflow moves work between teams, removes an approval, or turns a two-day report into one that arrives every morning. Those changes need owners, controls and adoption, so we design them into the implementation rather than leaving them to the rollout email.
The founder
Decifer on LinkedInAmit Chopra
Founder, Decifer. Dubai, UAE.
- Reads every enquiry himself
- Replies within one working day
- Will say when AI is the wrong answer
You will deal with the person who builds the work.
I started Decifer because businesses are drowning in information and short of understanding. The first answers were our own products. Running them in production taught me what it takes to keep AI working after the demo, and other businesses began asking for the same thing.
I am in Dubai, I read every enquiry myself, and I will tell you when AI is the wrong answer. Sometimes the fix is a spreadsheet formula and one fewer approval step.
Not ready to talk yet
All three toolsIs automating this task worth the money?
Enter the hours, the salary and the costs. Get the yearly saving, the payback period and a plain verdict, with every step of the arithmetic shown.
Automation payback calculator
Is my app safe to put in front of real users?
The six security checks we run on any codebase we inherit, as a scorecard. Answer honestly and get a fix list for anything failing.
Launch safety check
Do I need an AI agent, or something simpler?
Three questions about the task, one verdict. Many agent projects should have been simpler automations; this tells you which side yours is on.
Agent or automation?
Free, no signup, nothing stored. Each runs in your browser, uses your numbers, and ends with a recommended next step.
Questions buyers ask us
Ask a different oneWhat does Decifer do?
Decifer helps companies implement AI inside real business processes. We identify the opportunity, redesign the workflow, build the system, connect the tools you already run, establish operating controls and measure the result. We also build and run three public products of our own, which is where the method is tested.
Where should we start?
With a process where the outcome can be measured: repeated manual work, high volume, slow response times, fragmented information, or decisions that keep needing the same context. The two-week assessment ranks these before anything is built, at a fixed fee credited in full against any build that follows.
We already ran a pilot and it stalled. Can you take it over?
Often, yes. The assessment works on an existing pilot as well as a new idea. We baseline the process, find the point it stopped at, and set out the shortest route from there to something running daily.
Can you work with our existing systems?
Yes. Most of the work involves existing environments. We assess the available APIs, databases, documents and permissions before deciding how the implementation connects to them.
Do you build AI agents?
Yes, where the workflow benefits from one. Where plain automation or a simple lookup does the same job, we build that instead: it costs less every month and your team can own it. We have made that swap 5 times in our own systems.
How do you deal with AI errors?
We design around them. Figures are checked against what the code calculated, actions are limited to what the job needs, a person reviews anything expensive, every step is logged, and unusual cases have a route out. The system will not publish a number it did not work out. How tight the controls are depends on what a wrong answer would cost you.
How do you calculate ROI?
The baseline is taken before implementation: employee time, processing cost, turnaround, error rate, conversion or another operating measure. After deployment the same measures are read again, the same way. Without a baseline, ROI is an opinion.
Who owns what gets built?
You do. Every account is opened in your name, the repository transfers to you at handover with a runbook, and the data lives in standard Postgres you can export. Ongoing support is a commercial choice, never a technical trap.
Can you show us named client work?
Where a client has agreed in writing to be named, yes. Otherwise we describe work by sector and shape, with what we built, what changed, how it is measured and where a person stays in charge. Your project would be treated the same way. Figures are published only with the method and written permission.
Make the next AI project one the business can measure.
Thirty minutes with the founder, no slides. You will know what the right solution looks like, what it would take to build, what it should return, and which part to start with.
Replies come from a named person in Dubai within one working day.