AI Readiness Assessment in two weeks, with a costed first build
We run an AI readiness assessment of your systems in two weeks, then rank use cases and cost the first build.
Our search work at meinUnterricht improved search success by roughly 9% in a controlled experiment.
The offer, in full
- Fee
- Fixed fee, agreed before we start
- Duration
- Two weeks
- You receive
- 4 written deliverables
- What we need
- Read access to code, data and documentation
- Typical start
- 2 to 4 weeks from signature
- Ends with
- A working session and the written assessment
NDA before any access. Read-only, least-privilege, and agreed with your technical contact before anything starts.
01Trust
Engineering experience behind your assessment
We bring software engineering experience to the choice of AI use case and the first-build estimate.
- 120+Projects delivered across 12 countries since 2012
- 100+Senior in-house engineers, six countries
- 5.0Unified rating across 61 client reviews on Clutch
- ISO 27001Certified security practice, audited by Quay Audit UK
02Overview
Plan the first AI investment in your existing systems
unicrew's AI readiness assessment services turn a technical review of your systems and data into an investment decision. We inspect the architecture, data access and integration points, then establish what to keep, rebuild or remove. The findings identify which use case to prioritize and what the first build costs. We assess existing software in logistics, fintech and healthcare.
03The answer
Choose AI priorities with a costed plan
Self-report tools use your answers to recommend priorities. Internal engineers can inspect the systems directly. Our review adds an independent assessment, a defined two-week scope and a costed first build.
- Question 01
Which AI use case should we fund first?
We score candidate use cases on expected return and delivery risk, then rank them to guide the investment decision.
- Evidence
- A ranked use-case shortlist
- Question 02
Which systems and data need to change?
We assign every system and data source in scope to keep, rebuild or remove, with the blockers to an AI build named.
- Evidence
- A keep, rebuild, or remove map
- Question 03
What will the first AI build cost?
We define the top-priority use case's scope and sequence, with a team shape, timeline and cost.
- Evidence
- A costed plan and estimate
Use the assessment to decide your next step
You receive the full assessment. The findings and estimate are yours to use with unicrew or your own engineering team.
04Fit
Match the assessment to your AI investment
The assessment fits teams choosing an AI investment. These situations show when to assess first and when another unicrew offer can take the work forward.
Build a clear AI investment case
Good fit- You run software the business depends on and want to integrate AI into it.
- You want to understand what a stalled AI initiative or an unfinished pilot needs to reach production.
- Your data spans several systems and you need to establish its ownership, quality and accessibility.
- Your board, investor or enterprise buyer needs an AI plan backed by technical findings.
Start with a defined delivery need
Better first step- You have a defined conversation use case to test on real data. Start with the Conversation Intelligence Pilot.
- You have a ranked, costed AI roadmap ready for delivery. A dedicated development team can execute it.
- For infrastructure costs, choose the Cloud Cost and AI-Readiness Audit. For a buyer's security review, choose the Security and Compliance Readiness Sprint.
- Your production system needs stabilizing before an AI build. Start with Legacy Software Rescue.
In two weeks, we mark the systems and data sources in scope as keep, rebuild or remove. Then we rank the AI use cases by return against risk, and produce a costed plan for the top one.
Oleksandr TrofimovChief Technology Officer05Deliverables
Four documents for your AI investment decision
The four documents connect the technical findings to the recommended first build.
- 01
A keep, rebuild, or remove map
We place every system and data source in scope in one of the three positions and name its blockers.
- Format
- System-by-system table
- 02
A ranked use-case shortlist
We score candidate AI use cases on return and risk, then rank them.
- Format
- Scored, ordered shortlist
- 03
A data and API readiness audit
We read the schemas to establish who owns each data source, who can access it and whether it is clean enough to build on.
- Format
- Findings log and remediation list
- 04
A costed first build
You receive the scope, sequence, team shape, timeline and cost for the top-priority use case.
- Format
- One-page plan and estimate
From systems to priorities to a costed build
In this illustration, numbered tiles mark priority use cases and dark tiles mark systems to retire.
06Delivery
How we complete the assessment in two weeks
We review the systems with your technical owner, agree priorities with the budget holder, and present the assessment to your decision-makers.
- Kickoff and accessWe confirm the systems, data sources and integrations in scope. Before starting, we sign an NDA and agree read-only, least-privilege access with your technical contact.You getScope confirmed in writingFrom youA named owner, and read access to the code, data, and documentation in scope
- AuditWe inspect the architecture, data and integration points, then map candidate use cases to your business objectives.You getA findings logFrom youShort interviews with the people who know the systems best
- Prioritize and costWe score the use cases with your budget holder, then turn the top priority into a costed, sequenced plan.You getA ranked shortlist and an estimateFrom youOne prioritization call, with whoever holds the budget
- ReadoutWe hand over all four documents and review the findings, priorities and first-build estimate with your decision-makers.You getThe written assessmentFrom youA working session with the decision-makers present
07Proof
AI and systems experience behind the assessment
Our AI builds connect models to usable data, existing systems and human review.
- We build and operate AI productsWe build, run and sell Snaplore and Talkmetry, our own AI products. Snaplore uses Whisper, GPT models and AssemblyAI; Talkmetry uses GPT and AssemblyAI for call intelligence in HubSpot. An internal agent we built improved timely work-time logging compliance by 30%. That production experience informs the assessment's data and integration decisions.2AI products we build, run, and sell
- We connect AI to working systemsOur track record covers 120+ projects across 12 countries since 2012. In one human-in-the-loop document-recognition build, two people entered about 5,000 data sets a month manually. The component doubled that throughput, with an operator correcting or releasing the recognized data before it entered the database.120+Projects delivered since 2012
- Engineering experience informs the estimateOur 100+ senior in-house engineers work across six countries. unicrew has ISTQB-certified QA engineers. Our practice is ISO 27001:2022 and ISO 9001:2015 certified. The estimate connects the technical findings to the proposed scope and delivery sequence.100+Senior engineers, in-house
08Case studies
AI products and integrations we have built
Our search work uses AI tagging and embeddings to support a move to vector search. Knowledge capture and operator-reviewed document recognition show how AI connects to working systems.
See all case studies
Built by unicrewAI-powered knowledge managementSnaplore is unicrew's own product, built and operated in-house. It uses AI to transform how organizations document, structure, and share information, making meetings, training, and project discussions instantly accessible and actionable.Up to 60%Less time on documentation- AutomationDoubling data-entry throughput with a custom recognition toolunicrew automated a manual data-entry bottleneck with a C# and AWS recognition tool, doubling the data sets the team processed each month.2xData sets processed per month
09Client voices
Client results in AI and software engineering
Our headline result was a controlled experiment showing a roughly 9 percent lift in search success rate. The features built are live in production, and the AI tagging and embeddings work set up our move to vector search. What stands out most is their ability to own work end to end, from user-facing search features to the AI and data layer underneath.
Site was migrated and re-written, the new system is much more stable, and performance highly improved. Excellent technological level. highly responsive and communicative. They are highly committed to the project and business goals.
Our platform has been refactored to Laravel ensuring the code base is more stable, easier to maintain and easier to add new features. Their professionalism and quality of work have stood out in the partnership. We have had the code audited by a 3rd party who was extremely complimentary of the work.
Our business simply wouldn’t function without the Order Management system. We have made numerous functional and performance enhancements that has increased productivity and allowed us to maintain double digit growth without a substantial increase in staffing.
We’ve seen a 15-20% increase in revenue, which is through the efficiency gains and the ability to accurately track time on a permanent basis rather than just by a quarter hour or an hour. I’ve worked with a lot of development teams over the years and these guys have been the best. It’s nice to finally find a team that we can work with.
Scope your AI Readiness Assessment
Tell us which systems are in scope and what you want AI to achieve. We confirm the scope, fee and start date on the call.
What happens after you contact us
- We reply within one business dayA senior engineer reads what you send, not a bot.
- A short scoping callWhich systems are in scope, and what you want AI to do inside them.
- Scope and fee, in writingThe scope, the fee, and the week we can start. Most engagements start within two to four weeks.
- NDA, then kickoffSigned before any access is granted.
10Questions
Questions about your AI assessment
Confirm the assessment's scope, access requirements and next steps before booking.
An AI readiness assessment reviews the systems, data and integrations behind a proposed AI investment to establish what can support the build and what needs to change.
Our assessment takes two calendar weeks and produces four documents:
- a keep, rebuild or remove map of the systems in scope
- a shortlist of use cases ranked by expected return against delivery risk
- a data and API readiness audit
- a costed plan for the first build
The assessment has one fixed fee, agreed in writing before we start and unchanged at the end.
We quote it on the scoping call, based on the systems and data sources in scope. It covers the full two weeks, including all four documents and the readout.
An unusually large estate is discussed at scoping. There is no hourly billing or open-ended discovery.
The assessment takes two calendar weeks, with four stages ending in named outputs.
You provide:
- a named owner who can arrange read access
- short interviews with the people who know the systems
- one prioritization call with whoever holds the budget
- decision-makers in the room at the readout
Across our fixed-scope offers, the total is between four and ten hours of your team's time, depending on the offer. We carry out the assessment work.
A data readiness assessment examines whether the data can support the intended use. Our AI readiness assessment also checks the surrounding systems and integrations, then prioritizes and costs the first build.
We read schemas, pipelines and access paths. If data remediation needs to come first, the plan identifies it and routes the work to data engineering.
Our assessment covers four technical and commercial components: data, systems, use cases and the costed first build.
- Data readiness establishes who owns each source, who can access it and how clean it is.
- Technical readiness examines the architecture, integration points and codebase.
- Use-case readiness ranks candidates by expected return and delivery risk.
- Commercial readiness defines the scope, sequence and cost of the first build.
Broader maturity frameworks also assess talent, culture and governance. Our technical scope covers data ownership and access; it does not add an enterprise-wide organizational assessment.
We need read access to the relevant repositories, schemas or data dictionary, plus any API documentation.
NDA before any access. For the assessments and audits we work from read-only, least-privilege access, agreed with your technical contact before anything starts. Nothing is changed or deployed during the two weeks.
For a buyer's security review or regulated-data obligations, choose the Security and Compliance Readiness Sprint.
The assessment sets out what needs to change before the proposed AI build can proceed.
The four documents still give you a decision. The map identifies blockers and the data and API audit records the work needed before you build. You can address those findings first, then use the costed plan to decide when to proceed.
You receive all four documents and can use them with unicrew or your own team, without committing to a follow-on engagement.
You can:
- fund the first build using the scope and estimate
- address the map's blockers, then build
- give the assessment to your own engineering team to build from
Build the first use case with us and that work is AI integration: the estimate becomes the starting point, so you never pay for the same discovery twice.
11Where to go next
Take the next step in your AI plan
Compare all six fixed-scope offers, or choose the service that implements the assessment's recommendations.
Test a defined voice or call-AI use case on your own data.
Review cloud costs and the platform's capacity for AI workloads.
Prepare the security evidence an enterprise buyer needs to review.
Build a first release around a defined scope in six weeks.
Stabilize the production system before adding an AI feature.
Develop an ongoing AI strategy across business and technology decisions.
Build the first AI use case inside the systems you already run.
Implement the data and API readiness plan, including pipelines and schemas.
Rebuild the systems identified in the assessment's keep, rebuild or remove map.