Advise & Run · 1–2 weeks
Find the AI projects worth doing before you spend on them
A fixed-price, 1–2 week assessment of your processes, data, and systems. You get a shortlist of AI use cases scored by value, feasibility, and risk, a clear view of what your systems need to work with AI agents, and a costed roadmap with the first project to test.
Everyone wants AI. Few know where it pays.
Too many ideas, no ranking.
Every team has a use case. Without scoring them on value, feasibility, and risk, the loudest idea wins.
Systems that AI can't reach.
Key data sits in scanned files, spreadsheets, or systems with no API, and nobody has decided what an AI assistant would be allowed to see or do.
Pilots picked by enthusiasm.
Projects chosen because they demo well tend to stall when they meet real data, real permissions, and real costs.
What we do
A short, structured look at where AI fits in your business
We talk to the people who run your processes, look at samples of your real data, and review the systems an AI solution would need to work with. Then we score each candidate use case on the value it could create, how feasible it is with your data and systems today, and the risks involved. You finish with a shortlist, a readiness check, and a roadmap you can budget against.
- Interviews with process owners, IT, and the person who holds the budget
- A review of real data samples, not just descriptions of them
- A check of your systems: APIs, data access, permissions, and audit trails
- Agent readiness: what an AI agent would need to read from or act in each system, including standards such as MCP
- Plain-language scoring, so non-technical leaders can make the call
- A recommended first project, scoped as an AI Proof of Value
What you get
Use-case shortlist
The candidate use cases, each scored on value, feasibility, and risk, with the reasoning written out.
Data readiness review
Where the data for each use case lives, its quality, and what would need to change.
Systems and agent readiness
Which systems an AI agent could safely work with today, and what is missing: APIs, permissions, audit logging, or an MCP connection.
Costed roadmap
A sequence of projects with budget ranges and timelines, separating quick wins from larger builds.
First project, scoped
The top use case written up as an AI Proof of Value, with a success threshold and the data it needs.
Readout session
Findings presented to your leadership team, with time for questions.
How it works
- 1Days 1–3
Kickoff and interviews
Agree the scope, meet the process owners, and collect data samples and system documentation under NDA.
- 2Days 3–6
Data and systems review
Examine the data samples and check how each relevant system can be reached, what it exposes, and who may access what.
- 3Days 6–8
Scoring workshop
Review the candidate use cases with your team and score them together on value, feasibility, and risk.
- 4Final days
Roadmap and readout
Deliver the shortlist, readiness findings, and costed roadmap, and present them to your decision-makers.
Typical duration: 1–2 weeks, depending on the number of processes and systems in scope.
Good AI projects start with a hard look at the facts
Some of our most important work started with this kind of look at the facts, before any code was written.
18%
Share of claims that need the AI arbiter in our claims AI; the rest are decided by plain rules
Development-set routing counts
83%
Of 65 untouched held-back claims received a decisive and correct assessment, with zero false AI rejections
Holdout run frozen at the proof-of-concept tag
177
Functional requirements, each traced to code by requirement ID
Specification and project README
6 of 8
Insurer APIs verified on staging midway through delivery, with the remaining two requested from the client
Progress report, 12 August 2026
Case study
AI-Powered Claim Verification System
Before building anything for a health insurance administrator, we analyzed more than 11,000 historical claim rejections. About three quarters of the causes were detectable from a single claim's documents and data, which set the scope.
Case study
Smart IVR
An insurer's raw workbook of phone menus became a normalized menu tree and branch directory, with gaps and inconsistencies called out for decision before development started.
Case study
CSat: Customer Satisfaction and NPS Surveys
Three weeks of workshops with an insurer produced a specification of 177 functional requirements, each marked as stated by the client or derived by us.
Why Biznovelty
Engineers, not just advisers.
The people who assess your systems build and operate production software for insurers and other operations-heavy businesses.
We check the systems, not just the ideas.
We have connected software to insurers' core systems, helpdesks, SMS gateways, and phone systems, so we know what “ready” looks like.
We separate AI from rules.
In our claims AI, plain rules decide most claims and AI handles the rest. We'll tell you where a simpler solution is the better one.
A plan you can act on.
Every recommendation comes with a budget range, a timeline, and a first step small enough to test.
Engagement & pricing
Fixed price, fixed scope
1–2 weeks, covering an agreed set of processes and systems. Quoted after a 30-minute call.
Credited toward the next step
The fee is deducted from an AI Proof of Value or a build if you go ahead with us.
Confidential
NDA before any data or documents are shared.
Frequently asked questions
The people who run the processes in scope, someone who knows your systems, and the person who makes the budget decision. Usually a few hours each across the two weeks.
Not sure where AI fits? Start here.
In a 30-minute call we'll agree the processes to look at, the people involved, and what the assessment would cost.