AI Proof of Concept on Real Data, Fast
Everyone says you need AI; nobody tells you where it pays off. Our AI Proof of Concept validates your use cases, data, and feasibility before major investment, backed by 13+ years of engineering experience and 70+ in-house engineers.
- Fast answer: a working AI POC in 2 to 3 weeks.
- Any AI type: generative AI, ML, vision, NLP, RAG and agents.
- Fixed price: $12,000 to $20,000, NDA and full IP.
- Proven expertise: 4.8/5 Clutch, ISO 27001, 70+ engineers.
What Is an AI Proof of Concept?
How an AI POC proves feasibility first.
An AI proof of concept is a short, fixed-scope project that tests whether an AI idea can meet your accuracy, cost and speed targets on your own data before you fund a full build. A proof of concept in general shows an idea can work in practice; an AI POC adds one more test: can it work on messy, real-world data?
What is PoC in AI development? It is the feasibility gate between an idea and an MVP, testing one use case against an agreed success metric.
Signs your AI idea needs a POC first.
A POC earns its place when leadership wants proof before funding, when nobody knows if your data is good enough, or when the cost per request is a guess. Whether you are testing a support assistant, a forecast or an AI picture generator, a POC answers it in weeks.
Six Questions Every AI POC Answers
Leadership asks the same questions before funding any AI idea. Every AI POC we deliver answers all six with measured numbers, not opinions.
Is it accurate enough?
Measured on unseen data against your target.
What will each request cost?
Cost per request at expected production volume.
Is it fast enough?
Response times your users will consistently accept and appreciate.
Is our data ready?
What your data supports today and what to fix.
What are the risks?
Privacy, security and failure cases written plainly.
Is it worth building?
Results compared with today's baseline.
Which AI POC Should You Run First?
Every AI POC answers one business question with one success metric at one fixed price. Among AI solution companies, few cover this many AI POC types with one in-house team.
Generative AI and Image POC
RAG and Chatbot POC
Prediction and Vision POC
AI Agent POC
Six AI Ideas Proven Before Production
Each idea below was proven or tested before it scaled, and names who it was built for where we can. They show how an AI POC de-risks the build that follows into production.
Inspection AI users did not trust
The AI fix: model rebuilt, tested and improved before shipping.
Search by meaning, not keywords
The AI fix: semantic search validated, then rolled out.
AI alerts in noisy plants
The fix: interfaces tested in industrial simulations first.
Smarter fraud checks
The AI fix: adaptive risk scoring with retraining.
Product visuals at scale
The advice: a pilot rescue plan with success criteria.
Which LLM is right for us?
The AI fix: Claude, GPT and Gemini compared on your tasks.
Why AI POCs Stall, and Our Fix
Gartner predicted at least 30% of generative AI projects would be abandoned after the proof of concept, citing poor data, weak risk controls, rising costs and unclear value. Our AI POC process tests each of those four risks.
Nine AI POC Development Services We Run
Choose the type of AI you want to test, and our AI POC development team scopes it within a day. Every POC ends with measured results and a clear recommendation.
AI Feasibility Assessment
Generative AI and Image POC
RAG and Chatbot POC
Machine Learning POC
Computer Vision and Document POC
AI Agent and Voice POC
LLM Comparison POC
AI Cost and Latency Testing
POC to MVP Transition
AI POC vs Prototype vs MVP
Use an AI POC to prove feasibility, a prototype to prove usability and an MVP to prove people will pay. As an AI consulting and implementation partner, we build all three and tell you where to start.
Our rule: prove the AI works before you design screens, and prove people want it before you scale.
The 3-Week AI POC Timeline
A focused AI POC takes 2 to 3 weeks, with a live demo at the end of every week. You can stop at any checkpoint if the data says no.
The AI POC Deliverables You Receive
An AI POC is only useful if it leaves you ready to decide and ready to build. All eight AI POC deliverables below are included in the fixed price.
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Working AI demo
Build a working AI demo to test core features and show how the solution handles real business tasks.
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Measured results
Track key performance metrics to measure accuracy, speed, cost, and value against your defined business goals.
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Baseline comparison
Compare AI results with current workflows to identify improvements in speed, quality, accuracy, and overall efficiency.
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Data readiness findings
Review data quality, access, gaps, and formats to identify what is needed before AI development starts.
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Production cost forecast
Estimate development, hosting, API, and maintenance costs to plan your AI budget with confidence.
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Risks and limits
Identify model errors, data risks, privacy concerns, and limits before moving the AI solution into production.
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Go or no-go recommendation
Review results, costs, risks, and business value to recommend whether the project should proceed further.
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Code and documentation
Deliver source code, setup guides, technical documents, and usage instructions for future updates and maintenance.
Secure AI POC on Your Real Data
An AI POC only means something when it runs on real data, so protecting that data is step one. You choose what data a secure AI POC uses, where it runs and when it is deleted.
ISO 27001 certified
Company-wide security management.
NDA before any data
Signed before samples are shared.
PII removed
Personal data stripped before testing.
Your cloud if preferred
Run inside your account and region.
Data deleted after
Sample data deleted after completion.
Honest testing
Unseen data only, failures reported.
Fixed price
No surprises, no scope creep.
100% IP ownership
All POC code and results are yours.
AI POCs for the Industries We Serve
Every industry has its own AI ideas worth testing first. Where an idea we built went on to production for a named client we mark it; elsewhere we list typical AI POCs.
Automotive
eCommerce
Industrial Operations
FinTech
Healthcare
Legal
SaaS
Manufacturing
Marketing
AI POC Results Signed Off by Our Founder
Your POC result shapes a much bigger budget, so every AI POC recommendation is reviewed at leadership level. If the data says no, you hear no, clearly, before you spend on an MVP.
Mukesh Ram
Founder and CEO, Acquaint Softtech
Our AI POC Process in Six Steps
Every AI POC follows six steps tied to the success criteria you approved on day one. That discipline turns an AI consultation into a decision your board can fund.
Frame the Question
Define one clear business question and establish measurable success criteria to evaluate the proof of concept against specific business objectives.
Prepare the Data
Select a representative sample of real-world data, validate its quality, and apply appropriate privacy and security controls before development begins.
Set the Baseline
Measure current task performance using relevant metrics to establish a reliable benchmark and clearly demonstrate improvements through the proof of concept.
Build the POC
Develop a focused proof of concept using the required model, prompts, or pipeline, supported by a simple interface for practical demonstrations.
Test and Measure
Evaluate accuracy, processing speed, and operating costs against defined targets using unseen, representative real-world data to assess performance and reliability.
Demo and Decide
Present a live demonstration with measured results, a clear go-or-no-go recommendation, and practical next steps for production development and deployment.
AI POC Examples That Reached Production
These four AI ideas were tested and improved before they scaled, then went live with the same team. Each AI POC example links to a published Acquaint Softtech case study.
Manual inspections are slow, subjective, and expensive. We engineered a high-velocity, AI-driven ecosystem for Good2Know that transforms grainy images into actionable intelligence. Scale your operations with the speed of AI and the security of an enterprise-grade architecture with Acquaint Softtech.
Kandy scales e-commerce brands, and it wanted its partner stores to recommend products based on real shopper behavior instead of static catalog rules. Through custom software development, Acquaint Softtech built a machine learning recommendation engine with behavioral data pipelines and real-time recommendation APIs. Once deployed across several stores, it brought clear improvements in customer engagement, average order value, and repeat purchases.
Operational improvement shouldn’t require dozens of disconnected tools. This custom ai mobile app by Acquaint Softtech delivers a single, intelligent experience to uncover inefficiencies, streamline workflows, and scale operational excellence.
Bitpanda needed more engineering capacity without slowing its fintech platform. Through IT staff augmentation, Acquaint Softtech embedded developers directly into its product squads and supported software development across the web platform and fraud detection. The result was steadier feature delivery, a more responsive investment experience, and faster, more focused fraud investigations.
Client Reviews of Our AI Implementation
Rated 4.8/5 across 79 verified Clutch reviews of our AI consulting and implementation. Below, clients answer the questions buyers ask when comparing companies that provide AI services.
Why Choose Us Among AI Consulting Companies
We combine AI consultation with implementation: the engineers who prove your idea build it into production. As AI consulting in India, we add fixed POC pricing and offshore value.
Fixed POC price
$12,000 to $20,000, agreed before work starts.
Honest go or no-go
From $30 per hour, dedicated $4,400 per month.
Proof you can check
Named clients and verified Clutch reviews.
Production-grade POCs
Same tools as live systems, no rewrite later.
One team, start to finish
From POC to MVP and production deployment stages.
Your plan, your IP
All POC code safely stored in your repository.
The AI POC Tech Stack We Use
We build every AI POC on the same production-grade tools we use for live systems. When your AI POC succeeds, it scales to an MVP without a rewrite.
Generative AI
Image generation
Machine learning
Computer vision
Language AI
RAG
Agents
Demo interfaces
Backend
Evaluation
Cloud
Containers
How Much Does an AI POC Cost?
An AI proof of concept costs $12,000 to $20,000 at a fixed price and takes 2 to 3 weeks. If it succeeds, production typically costs $30,000 to $60,000, built by the same team.
Free AI Consultation
- Early AI ideas
- Choosing the right first test
- No commitment
Fixed-Price AI POC
- Proving one AI idea
- Board and investor decisions
- 2 to 3 weeks
POC to Production
- Integration with your systems
- Milestone-based payments
- 8 to 12 weeks
What drives AI consulting cost
AI Proof of Concept FAQs
Thinking about testing an AI idea? These answers cover what a POC proves, how long it takes, the fixed price and what happens next.
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What is an AI proof of concept?
A small, focused build that tests whether an AI idea can reach the accuracy, speed and cost you need on your own data, delivered with a go or no-go report in 2 to 3 weeks.
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What is PoC in AI development?
The step that proves technical feasibility before you fund a full product. It tests one use case on real data against an agreed success metric.
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What is a proof of concept (PoC)?
A short, limited project that shows an idea can work in practice. It is smaller than a prototype or MVP and focuses on feasibility, not design or market demand.
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How much does an AI POC cost?
An AI POC costs $12,000 to $20,000 at a fixed price and takes 2 to 3 weeks. If it succeeds, production AI typically costs $30,000 to $60,000.
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What is the difference between an AI POC and an MVP?
An AI POC proves the AI can work on your data; an AI-powered MVP is a launchable first version that proves customers will use and pay for it.
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Which 3 jobs will survive AI?
No one can say for certain, but roles built on human care, hands-on skill and judgment, such as nurses, electricians and strategic leaders, are widely seen as most resilient.
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Can you build an AI picture generator POC?
Yes. We test image generation and editing for product or marketing visuals, checking quality, brand fit, cost and licensing before you scale.
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Do you offer AI consulting and implementation together?
Yes. The same in-house engineers who prove your idea build it into production, so there is no handoff between advice and delivery.
Related AI Development Services
AI POC clients often move next to an AI-powered MVP, custom AI solutions or AI consulting. Explore the AI development services below from the same team.
AI Engineering
09AI Specialization
10- • Machine Learning Development Services
- • Computer Vision Development Services
- • NLP Development Services
- • LLM Fine-Tuning Services
- • RAG Development Services
- • AI Agent Development Services
- • Agentic AI Development Services
- • AI Chatbot Development Services
- • Voice AI & Chatbot Development Services
- • Multi-Agent AI Services
India (Head Office)
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Canada
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