Your data · 1–3 weeks · Measured results
Why Start With an AI Proof of Concept
Most AI initiatives stall somewhere between the demo and production. Industry surveys through 2026 consistently show that most AI pilots never reach production. The usual causes are unclear success metrics, data that wasn't ready, or integrations nobody tested early.
An AI POC Sprint answers the one question that matters before you invest: does this AI approach work on your data, with acceptable accuracy, speed and cost? In one to three weeks, you get a working proof of concept, measured results and an honest go/no-go recommendation.
Who It's For
SMBs exploring whether AI can automate a costly manual process
Enterprise innovation teams that need evidence before securing budget
Product companies adding an AI feature to an existing app
Founders who need a working AI demo for investors or early customers
Operations leaders deciding whether to build or buy an AI tool
What We Build in a POC
AI Agents
Agents that take actions across your tools, such as updating your CRM, triaging tickets, qualifying leads, scheduling or running internal workflows.
RAG Chatbots and Knowledge Assistants
Assistants that answer questions from your documents, help centre, policies or product data, with citations.
Document Processing
Extraction and classification from invoices, contracts, forms, claims and reports.
Workflow Automation
AI steps inside business processes, using tools such as n8n, APIs and custom orchestration.
Predictive and Classical ML
Forecasting, scoring, recommendation and anomaly detection models.
Computer Vision
Image and video classification, detection and inspection.
What You Get
Working POC running on a representative sample of your real data
Evaluation report covering accuracy, latency and cost per run against the agreed success metric
Demo session for your stakeholders
Production architecture showing what it takes to scale, including integrations, monitoring, security and hosting
Effort estimate for the pilot and production phases
Go/no-go recommendation, including "no" when the evidence says so
Source code for the POC
How It Works
Step 1: Scope (Days 1–2)
We choose one use case and one success metric, for example "extract 12 fields from supplier invoices with 95% accuracy". We also agree the data sample, access and constraints.
Step 2: Data Review (Days 2–3)
We assess data quality and gaps early. If your data isn't ready, you find out on day three, not in week ten.
Step 3: Build (Weeks 1–2)
We build the core AI capability: model selection, prompting, retrieval, tools and a simple interface for testing it.
Step 4: Evaluate (Final days)
We test against a held-out set and the agreed metric. We also measure cost per run, so there are no surprises at scale.
Step 5: Demo and Report
A live demo, a walkthrough of the results and a plan for the next step.
Pricing
POC type | Duration | Fixed price |
Single-purpose AI feature (e.g. FAQ assistant, document extraction) | 1 week | from $2,000 |
AI agent with tool integrations | 2 weeks | from $4,500 |
Multi-step or multi-source AI workflow | 3 weeks | from $8,000 |
POC fees are credited against a Paid Pilot started within 60 days.
What Makes a POC Succeed
One metric: a POC that tries to prove everything proves nothing
Real data: synthetic data hides the problems that kill production systems
Cost awareness: a model that works but costs too much per run is not a success
A named owner: someone on your side who will carry the result forward
A production view from day one: we design the POC so it can grow rather than be thrown away
POC vs. Pilot vs. Production
POC | Pilot | Production | |
Question answered | Can it work? | Does it create value? | Can it run reliably at scale? |
Users | Internal testers | Limited real users | All users |
Data | Sample | Live, limited | Live, full |
Duration | 1–3 weeks | 4–8 weeks | Ongoing |
Why Codersarts
Hands-on experience building AI agents, RAG systems, document automation and ML models
Model-agnostic: we use OpenAI, Anthropic, Google or open-source models, chosen by fit and cost
500+ apps shipped since 2018, so POCs are built by engineers who ship production systems
Fixed price, weekly updates and full code ownership
Global delivery across time zones
Data Security
We work under NDA, use the minimum data needed and can run the POC inside your own cloud environment when required. Sensitive data can be masked or anonymised before it is shared.
What Happens Next
Paid Pilot: put the POC in front of real users
Pilot-to-Production: harden the system and deploy it
Dedicated Team: for a longer AI roadmap
Stop: with evidence, not guesswork
Frequently Asked Questions
How is a POC different from an MVP?
A POC proves technical feasibility for one capability. An MVP is a usable product for real customers.
Do I need clean data?
No. Part of the POC is telling you how ready your data is and what it would take to fix it.
Which AI models do you use?
Whichever fits your accuracy, cost, privacy and hosting requirements.
Can the POC code be reused?
Yes. Where possible, we build POCs on production-ready foundations.
What if the POC fails the metric?
You get a clear explanation of why, and whether a different approach could succeed. A fast, inexpensive "no" is a valuable outcome.