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AI · Proof of Concept

Prove your AI idea
before you bet the roadmap on it.

We turn a promising AI idea into a working, production-minded proof of concept in weeks: with real data, honest metrics, and a clear go/no-go decision at the end.

poc-sprint · week 3 of 4 ● ON TRACK
✓
Frame
Hypothesis & metrics
✓
Build
Working prototype
3
Test
Real-data evaluation
4
Decide
Go / no-go
✓
Decision at day 24: tested on 12k real records, 88% accuracy, projected ROI in 6 months. Recommendation: GO.
2–4 wks
Idea to working POC
Fixed
Scope, timeline & price
Real data
Tested on your data, not demos
Go/No-go
A clear decision at the end
What is an AI POC?

A small, working build that answers one question.

A proof of concept tests whether a specific AI idea is feasible and valuable, on your real data: before you commit budget, headcount, and roadmap. It's the fastest, cheapest way to replace "we think" with "we know."

We keep the slice deliberately small and the metrics honest, so you get a defensible go/no-go in weeks.

Slideware
Vendor demos on fake data
Slideware
No real answer on feasibility
Real POC
Runs on your real data
Real POC
Honest metrics & a decision
Why it matters

What a POC changes before you commit.

Evidence over opinion, so the big decision is an easy one.

Fast validation

Know in weeks, not quarters, whether an AI idea is worth building.

De-risk the spend

Prove feasibility and ROI before committing budget and roadmap.

Real evidence

Metrics on your data replace opinions and vendor promises.

A running head start

A production-minded POC becomes the foundation, not throwaway code.

How it works

Frame, build, measure, decide.

STEP 01

Frame

Pin the hypothesis, success metrics, and the smallest slice that proves value.

STEP 02

Build

Stand up a working prototype on your real data with the right models and tools.

STEP 03

Measure

Test against the success metrics with honest, side-by-side evaluation.

STEP 04

Decide

A clear go / no-go with cost, risks, and the path to production.

What we build

POCs across the AI landscape.

Agent & Copilot POCs

Prove an Agentforce or copilot use case resolves real work end-to-end.

Chatbots & Assistants

Validate a conversational assistant that answers from your own knowledge.

Document Intelligence

Extract and structure data from your real documents.

Predictive & Scoring

Score, forecast, or classify on a sample of your data.

RAG & Knowledge Search

Ground answers in your content with retrieval pipelines.

Built for a real answer

A POC that decides, not just demos.

Anyone can wire up a flashy demo. We design the POC so its result is defensible: agreed metrics up front, your real data, honest evaluation, and code built to scale if the answer is yes.

Defined success metrics
We agree what “good” looks like before we build.
Your real data
Tested on representative data, not cherry-picked demos.
Honest evaluation
We report what works and what doesn’t, including no-go.
Production-minded
Built so a green light can scale, not be thrown away.
The sprint

From hypothesis to decision in weeks.

01

Discovery & hypothesis

Days 1–3

Workshop to frame the use case, data, and success criteria.

02

Data & setup

Week 1

Access, sample data, environment, and model selection.

03

Build the POC

Week 2–3

Iterative development of the working prototype.

04

Evaluate & demo

Week 3–4

Measure against metrics and demo on real scenarios.

05

Decision & roadmap

Wrap

Go/no-go, findings, cost model, and production plan.

What you walk away with

Every POC ships these.

Working prototype

A functional POC you can click, test, and show stakeholders.

Evaluation report

Metrics, benchmarks, and side-by-side comparisons.

Feasibility & risk assessment

Technical, data, and cost risks laid out plainly.

Business case

ROI model and the value of moving to production.

Production roadmap

Architecture and a phased plan to scale the POC.

Stakeholder demo

A guided walkthrough to align decision-makers.

The stack Model-agnostic, the right tool for the question.
Anthropic Claude OpenAI Agentforce Einstein / Models API LangChain Python Vector DBs RAG pipelines Salesforce Flow REST APIs Streamlit AWS / Azure
POCs in the wild

Ideas we've validated fast.

Financial Services

Fraud-signal copilot

POC scoring transactions and surfacing rationale to analysts.

2 wks to validate
Healthcare

Clinical note summarizer

POC summarizing encounters with clinician review.

88% draft accuracy
SaaS

Support deflection agent

POC answering tier-1 tickets from your docs.

3x faster resolution
Manufacturing

Maintenance prediction

POC forecasting failures from sensor data.

4 wks to a decision
Why iTech for a POC

A partner who'll tell you the truth.

Weeks, not quarters

A fixed, fast sprint that respects your time and budget.

AI + Salesforce depth

We build POCs that fit your real stack, not a lab.

Honest answers

We’ll tell you when the answer is no, and why.

Five-region delivery

Follow-the-sun teams across USA, UK, Europe, Middle East, and APAC.

FAQ

POC development, answered.

A proof of concept is a small, working build that tests whether a specific AI idea is feasible and valuable on your real data, before you invest in full production.
Most POCs run 2–4 weeks with a fixed scope, timeline, and price. We deliberately keep the slice small so you get an answer fast.
A working prototype, an evaluation report with metrics, a feasibility and risk assessment, a business case, and a production roadmap, plus a clear go/no-go.
That’s a valid, valuable outcome. Learning an idea won’t work in a few weeks saves months of spend. We report honestly, including no-go, with the reasons.
Yes. We test on representative samples of your real data under your security and privacy requirements, not staged demos.
That’s the goal. We build production-minded so a green light scales into a full implementation instead of being thrown away.

Have an AI idea? Let's prove it.

Book a free scoping call. We'll shape the hypothesis, define success, and give you a fixed plan for a POC in weeks.