Success defined on day one
We agree the numbers that mean "go" before we write any code, so the result can't be argued afterwards.
Prove your AI idea on real data before you fund the build
Backendz builds focused AI proofs of concept and MVPs in weeks, not quarters. You get a working prototype, measured against targets you agreed up front, and an honest go, adjust or stop.
Or see how we engage first — four shapes, no sales call required.
- Use-case discovery
- Data readiness checks
- Feasibility studies
- Rapid prototyping
- Model comparison
- Accuracy benchmarking
- Cost-per-run estimates
- MVP builds
- User testing
- Success criteria
- Go/no-go reports
- Production roadmaps
- Document AI
- Agent prototypes
- Vision prototypes
- Forecasting prototypes
We stop AI budgets being spent on hope
No more guessing which AI idea will actually work
Forget vendor demos that only work on their data, not yours
Stop waiting for AI engineers you haven't hired yet
Done with pilots that run for months and never reach a decision
Skip the surprise of AI running costs at full volume
Say bye to tools bought, tried and quietly abandoned
Why choose Backendz for AI Proof of Concept & MVP?
Your data, not a demo set
Every prototype is tested on your own documents, records, images or footage.
We'll tell you if it won't work
A clear "stop" after six weeks is cheaper than a failed build after six months.
The same team builds it properly
If it's a go, the engineers who proved it take it to production.
We build AI Proof of Concept & MVP
Four ways this usually starts
Most enquiries land in one of these. If yours does not, say so on the call — we would rather shape the engagement than sell you the nearest shape we have.
Most teams start here
You have an idea and need proof
- One use case, one dataset, agreed success measures
- Working prototype in a few weeks
- Decision report with accuracy and running cost
Best for: Leaders who need evidence before a budget sign-off
You need to choose between approaches
- Two or three AI approaches tested side by side
- Same data, same scoring, clear comparison
- Recommendation with trade-offs on cost, speed and accuracy
Best for: Teams torn between options or vendors
The idea is proven, users need to try it
- MVP with a simple interface and real logins
- Released to a small group of live users
- Usage and feedback tracked from day one
Best for: Founders and product teams ready to learn from real use
You're not sure your data is ready
- Data audit across quality, volume and access
- Gaps and fixes listed in plain language
- Realistic view of what AI can do with what you have
Best for: Companies early in their AI journey
Ideas that became systems in production
Every card is a build we shipped and still support. Open one for the decisions behind it.
We serve the best in the industry
All of it, written down
What falls inside this service. Nothing here is an upsell discovered later.
What we prove
- Document extraction
- Question answering on your documents
- Task automation with agents
- Image and video detection
- Forecasting and prediction
- Classification and routing
What you get
- Working prototype
- Test results on unseen data
- Running cost estimate
- Risk list
- Production roadmap
- Go/no-go recommendation
Keeping it honest
- Success criteria agreed up front
- Held-back test data
- Side-by-side comparison with today's process
- Documented limitations
Stack
- Python
- PyTorch
- Claude and OpenAI models
- Open-source models (Llama, Qwen)
- YOLO11
- FastAPI
How it works
We agree a single use case, the data to use and what success means in numbers.
From the people who use it daily
Operations directors, CTOs and plant managers — the ones still using the system a year after the invoice cleared.
Freight operator · 14 depotsFreight operator · 14 depotsWe wanted answers our team could check against the original document. Having the relevant clause beside the response makes the assistant useful when reviewing policies and contracts.
Our rental journey had to work for both vehicle owners and travellers. Backendz brought listings, availability and bookings together into a platform that feels straightforward to use.
A single headcount does not tell us where congestion is developing. The zone-based view gives our team a clearer picture of how people are moving and where attention may be needed.
Tender preparation involves gathering evidence and getting the right people to review each section. Backendz brought those steps into one workspace, with drafts our team can check and approve.
Our estimating process needed to reflect how we actually price work. Backendz took time to understand the inputs and rules, then built a quoting journey our team could review and refine.
The team took time to understand how our staff work between appointments. The resulting system brings scheduling and client information together without making the day more complicated.
We could review working features throughout the build and give specific feedback. That made it easier to keep the product focused on the people using it.
Our field team needed access to job details away from the office. The mobile workflow keeps the relevant information close at hand and gives the office a clearer view of progress.
We wanted a client portal that reflected how we deliver our services. Backendz brought documents, requests and progress updates into a straightforward experience for clients and staff.
Our teams were entering the same information into several tools. Backendz mapped the process with us and built a more connected workflow with clear review points.
We needed scheduling, availability and shift changes in one place. Backendz shaped the system around the way our operations team works and stayed involved after launch to help us refine it.
Ask for references and we will put you on a call with the team that runs the system — not a case study PDF.
References available on request
We wanted a practical way to review PPE and safety-zone events using our existing cameras. The team focused on the situations our site managers need to investigate and made the footage easier to review.
The priority was a counting workflow our warehouse team could use during a normal shift. Backendz connected image-based counts with the records we already use and made discrepancies easier to review.
Our information was spread across several operational systems. Backendz designed a clearer way to ask questions, inspect the supporting evidence and keep approval with our team before an action is taken.
The reconciliation workflow gives our finance team one place to review mismatches and supporting records. We can trace an exception back to its source and decide what needs attention.
We had outgrown the spreadsheets that held our pricing rules. Backendz helped turn that knowledge into a quoting tool we can maintain and explain to new colleagues.
Connecting our sales and inventory systems was the priority. The integration gives our team a clearer view of updates and a practical way to investigate records that need review.
The cloud work started with understanding our existing systems and dependencies. The team documented the setup and gave us a clearer basis for monitoring and ongoing maintenance.
The engineers worked alongside our team and explained their decisions clearly. Code reviews and practical documentation helped us stay involved throughout delivery.
Why Backendz
5 yrs
In production
Ops CopilotTender Response DrafterPolicy & Contract AssistantPallet CountingConstruction Site PPE & Safety MonitoringCrowd Density MonitoringDigital Security PlannerVirtual EstimatorJoblogyPamacraftsRollBnBVenus AestheticsShiftTrackerFreight DispatchInvoice Reconciliation AIChilled TraceabilitySteel Quoting EngineFactory Floor AppFleet Driver App
We quote fixed and we hold it
2
Platforms run in-house
Use-case discoveryData readiness checksFeasibility studiesRapid prototypingModel comparisonAccuracy benchmarkingCost-per-run estimatesMVP builds
Cloud Strategy & ArchitectureCloud MigrationCustom ERP DevelopmentInfrastructure ModernisationCloud EngineeringERP ImplementationCloudOpsERP IntegrationDevOpsSupport & Managed Cloud ServicesERP Customisation & ModulesCloud Cost OptimisationERP Data MigrationCloud Security & ComplianceAI & Edge InfrastructureERP Support & OptimisationSaaS Stack AuditCustom CRM DevelopmentCustom HR & Workforce ManagementCustom Job & Field Service ManagementCustom Client Portals & BookingSaaS Data Migration & ExitAI Proof of Concept & MVP
User testingSuccess criteriaGo/no-go reportsProduction roadmapsDocument AIAgent prototypesVision prototypesForecasting prototypes
AI Agent DevelopmentCustom AI Assistants & ChatbotsGenerative AIAI Workflow AutomationNatural language processingCustom Software DevelopmentComputer Vision DevelopmentWeb App DevelopmentMobile App DevelopmentAI Video Analytics for CCTVProduct DevelopmentMVP DevelopmentUX/UI DesignApplication ModernisationSoftware Testing & QAMachine Learning & Predictive AnalyticsERP & operationsAI Product DevelopmentData & cloudProduct & designAutomation & integrationsSupport & operations
45
Services, one team
Senior engineers, not account managers
24/7 production support and monitoring
Still running on workarounds?
FAQ
The things people ask before they commit.
Still have questions?
Ask it on the call. We would rather answer an awkward question early than discover it in month three.
Book a callWhat is an AI proof of concept?
An AI proof of concept is a small working version of an AI idea, tested on your own data to show whether it can do the job well enough to justify a full build. An AI MVP goes a step further: a usable first version in the hands of real users.
What is the difference between an AI proof of concept and an AI MVP?
An AI proof of concept tests whether AI can do a specific job with your data, measured against agreed criteria. An AI MVP is a usable first version released to real users to learn how they use it. A proof of concept proves it can work; an MVP proves people will use it.
How long does an AI proof of concept take?
Most AI proofs of concept take 4 to 6 weeks: about a week to define the use case and success measures, a few weeks to build and test, and a final decision report. How ready your data is makes the biggest difference.
How much does an AI proof of concept cost?
Cost depends on the use case, the data and the accuracy needed. Backendz scopes every proof of concept at a fixed price after a discovery call, so you know the total before work starts.
What data do I need for an AI proof of concept?
A representative sample of the data the AI will use in real life, such as documents, records, images or video. It doesn't need to be perfect. Checking data quality is part of the proof of concept.
What happens if the proof of concept fails?
You get a clear report on why, what would need to change, and whether another approach is worth testing. A failed proof of concept costs far less than discovering the same problem after a full build.
Can the proof of concept become a production system?
Yes. The same team takes it through to an MVP and a production system, adding security, monitoring and integration with your existing software.