AI Proof of ValueFind out if AI works on your data before you build it
A fixed-price, 2–4 week test of one AI use case on your real data. Build, adjust, or stop.
83%
Decisive and correct on held-back cases
- Cost per item
- ~1.6 taka
- False AI rejections
- 0
Failure categories
- Missing documents
- Unreadable scans
- Needs examiner judgment
Most AI pilots prove the demo, not the business case
Demos run on someone else's data.
Vendor samples are clean. Yours are skewed, handwritten, bilingual.
Nobody measures the failures.
Without a held-back set, “about 90% right” hides costly cases.
The cost shows up after launch.
Per-transaction costs and human review surface at the first invoice.
What we do
A short, measured test of one use case
We build the smallest working version on your real data, tune it, then measure once on unseen data.
- Success threshold agreed in writing first
- Your real data, under NDA
- A working prototype, not slides
- Accuracy, cost, and speed on held-back data
- Honest advice, including “don't build this”
What you get
Working prototype
A small app or API to try on new examples.
Evaluation report
Held-back accuracy by category, every failure explained.
Cost model
Cost per transaction at your volumes, across AI models.
Risk register
Where a person must stay in the loop.
Build plan
Production scope, timeline, and budget, if results support it.
How it works
- 12–3 days
Scope
Pick a use case and threshold; collect known-outcome cases.
- 2
Split the data
Set aside a held-back set nobody tunes against.
- 31–3 weeks
Build and tune
Improve the prototype on the development set only.
- 4Final days
Measure
Run the frozen prototype once on held-back data.
- 5
Decide
Readout: build, adjust, or stop.
What this looks like in practice
Our claims AI started this way: a measured test on real, already-decided claims, before any product code.
83%
Decisive and correct: 54 of 65 held-back claims, zero false AI rejections
Holdout run frozen at the proof-of-concept tag
~1.6 taka
Cost per claim end to end (about US$0.013)
Model benchmark and holdout cost ledger
~30×
Lower document-reading cost after benchmarking models, no loss of accuracy
Model benchmark
Case study
AI-Powered Claim Verification System
AI reads and assesses claim documents; the examiner makes the final decision.
Case study
one2one AI Support Agent Platform
AI agents answer customers from approved knowledge, measured on real questions.
Why Biznovelty
We measure before we promise.
Our claims AI and support agent run on real evaluation sets.
We read messy, real documents.
Handwritten Bangla and English prescriptions, two ID card designs, phone photos.
We build what we test.
The same team can take it to production and run it.
Engagement & pricing
Fixed price, fixed scope
Quoted after a 30-minute call: 2–4 weeks, one use case.
Credited if you go ahead
The fee is deducted from the build if you proceed.
Your data stays protected
NDA first, an approved environment, AI providers with retention disabled.
Frequently asked questions
A repeated, high-volume task where you already know the right answer for past cases: claims you have adjudicated, documents you have processed, or customer questions your team has answered.
Bring one process and a sample of real cases
A 30-minute call: does it fit, what data, what cost.