Validate the Technology Before Building the Product
Test technical feasibility — an integration, an AI approach, an architecture decision — before committing real MVP budget to a foundation that hasn't been proven. Fixed price from $2,000, documented findings, and a clear recommendation on what to build next.
Codersarts builds POC (Proof of Concept) development services for startups, founders, enterprises, and product teams who need to answer one specific technical question — can this work — before committing real budget to a full MVP or production build.
We scope focused POCs across four areas: API and integration validation (can two systems reliably exchange the required data), AI and ML evaluation (can an LLM, RAG pipeline, or model hit the required output quality), performance testing (can the architecture meet throughput and latency requirements), and data and architecture experiments (can a pipeline or service design handle expected scale). Each POC is deliberately narrow — built around the specific technical risk that could sink the project, not a smaller version of the full product.
Every engagement concludes with documented findings and a clear recommendation: proceed to MVP Development, adjust the technical approach, or stop before a larger investment. A POC is not production-ready software — moving validated technology into a hardened, scalable system is handled separately through Product Engineering.
Pricing is fixed and scoped to the specific question — from $2,000 for a focused API/integration test to $8,000 for a complex data or architecture experiment — typically delivered in 1 to 4 weeks. If your uncertainty is about the product experience rather than the technology, Prototype Development is the better starting point instead.
Validate the Technology Before Building the Product
Some of the most expensive mistakes in software happen when a team commits to a full MVP before knowing whether the underlying technology actually works. Codersarts builds focused Proofs of Concept (POCs) for startups, founders, enterprises, and product teams to answer one specific technical question — can this integration work, can this AI approach hit the accuracy bar, can this architecture handle the load — before real MVP budget is at risk.
A POC is intentionally narrow. It exists to resolve the technical risk that could sink the project, not to build a smaller version of the product.
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POC Pricing & Timeline
Fixed price, scoped to the specific technical question — not an open-ended research engagement.
Tier | What it validates | Timeline | Fixed Price |
API/Integration POC | Can two systems exchange the required data reliably? | 1–2 weeks | $2,000–$4,000 |
AI/ML POC | Can an LLM, RAG pipeline, or ML model hit the required output quality? | 1–3 weeks | $2,500–$6,000 |
Performance POC | Can the proposed architecture meet throughput and latency requirements? | 1–2 weeks | $2,500–$5,000 |
Architecture/Data POC | Can this data pipeline or service architecture handle the expected scale? | 2–4 weeks | $4,000–$8,000 |
Every POC concludes with a documented recommendation — proceed to MVP, change approach, or run another experiment — not just working code with no decision attached.
What a POC Answers vs What It Doesn't
A POC produces evidence and a decision, not production-ready software — and it's worth being upfront about that distinction before scoping one.
What it validates: functional feasibility, output accuracy, latency, throughput, reliability, integration behavior, resource requirements, and cost implications of a specific technical approach.
What it doesn't do: POC implementations are optimized for learning fast, not for running in production. A successful POC should not be deployed as-is — moving from POC to production typically needs architecture refinement, security hardening, error handling, scalability work, monitoring, and CI/CD, which is exactly what Product Engineering picks up once the technical approach is proven.
POC Development Process
1. Identify the technical question. We determine exactly what needs proving — can the API integration work, can the model hit the required output quality, can the architecture support the workflow — with clear success criteria defined before any code is written. A POC needs a specific question, not a vague mandate to "explore the technology."
2. Build the focused implementation. Only the components necessary to test the hypothesis get built: the specific API call, the model integration, the data pipeline, the architecture pattern — deliberately not the surrounding product.
3. Evaluate against the criteria set in step one. Functional feasibility, accuracy, latency, throughput, reliability, and cost — measured, not estimated.
4. Document the findings. What worked, what didn't, the technical limitations discovered, and a clear recommendation: proceed to MVP, change the technical approach, run a different experiment, or stop before a larger investment gets made on a foundation that won't hold.
Technical POC Capabilities
API & integration POCs — validate whether systems can actually communicate the way the product needs: third-party APIs, payment APIs, enterprise systems, authentication providers, webhooks, and data synchronization.
AI & ML POCs — test whether an AI approach delivers the required result before committing to full development. See Codersarts AI for the broader AI capability set once a POC has validated the approach.
Data & processing POCs — validate complex data workflows (ingestion, transformation, ETL, extraction, large-dataset processing) before they become load-bearing parts of a production application.
Architecture POCs — compare approaches when the architecture itself is the uncertainty: service architecture, event-driven workflows, distributed processing, or cloud architecture decisions with real cost implications either way.
Performance POCs — test whether a proposed approach can meet response time, throughput, and concurrent-user requirements. The goal is establishing technical viability, not delivering production-scale performance in the POC itself.
AI POC Development
AI projects carry more technical uncertainty than most software, which is exactly why a focused POC often pays for itself before a full AI MVP gets built.
AI Question | POC Focus |
Can an LLM produce the required output? | Model & prompt evaluation |
Can the system answer accurately from private data? | RAG experiment |
Can multiple tools be orchestrated reliably? | Agent workflow validation |
Can documents be processed accurately? | Document AI extraction test |
Can the model classify or extract structured data? | Classification / structured extraction |
Can predictions meet the required threshold? | ML evaluation |
Can AI operate within acceptable latency? | Performance evaluation |
See our AI MVP development guide for how a validated POC feeds directly into the right MVP architecture — LLM, RAG, or agent-based.
Prototype vs POC vs MVP
These three services answer genuinely different questions, and building the wrong one first is one of the most common — and expensive — mistakes founders make.
Prototype | POC | MVP | |
Answers | Can users understand and use this experience? | Can the proposed technology actually work? | Will users use and value the working product? |
Focus | User experience | Technology | Market |
Data | Often simulated | Experimental implementation | Real application data |
Scope | Limited functionality | Narrow technical scope | Broader product scope |
Output | Demonstrated concept | Technical viability evidence | Delivered customer value |
If your product's experience is the uncertainty, start with Prototype Development. If the experience is clear but the technology underneath it isn't proven, a POC comes first. If both are already settled, go straight to MVP Development.
When You Need a POC — and When You Don't
Build one when: the technology is unfamiliar, an integration is unproven, AI output quality is uncertain, performance requirements are unclear, a new architecture is under consideration, or a technical risk could sink the project if it turns out wrong after full investment.
Skip it when: the technology is already proven, a similar implementation already exists elsewhere in your stack, the architecture is well understood, or the real uncertainty is product-market fit rather than technical feasibility — in which case MVP Development is the more direct, faster path.
POC Development for Startups
A focused POC reduces technical uncertainty before a founder commits real MVP budget — validating a difficult assumption, comparing implementation approaches, and giving investors or co-founders technical evidence rather than confidence alone. The discipline that matters here: the POC stays scoped to the highest-risk question, not a general exploration of "what's possible."
POC Development for Enterprises
Enterprises use POCs to evaluate new technology before introducing it into existing systems — AI adoption, cloud migration experiments, process automation, or architecture validation — producing evidence before a larger implementation program gets funded internally.
From POC to MVP (and to Production)
Technical Question → POC → Technical Validation → MVP Scope → MVP Development → Real Users → Product Validation
A POC provides technical evidence. The MVP turns that validated approach into a real, usable product — see MVP Development Services for that next stage. And when the product is validated and needs to move beyond MVP-grade code into a hardened production system — security, scalability, monitoring, CI/CD — that's Product Engineering, not a POC or MVP extension.
Tech Stack
AI/ML: OpenAI GPT-4o, Anthropic Claude, open-source models, vector databases (Pinecone, pgvector)
API & integrations: REST, GraphQL, webhook testing harnesses, sandbox environments for third-party APIs
Data: Python (pandas, Airflow for ETL experiments), PostgreSQL
Performance testing: Load-testing tools scoped to the specific throughput/latency question being answered
Cloud: AWS, Google Cloud — scoped to whatever the architecture question requires, not a full production environment
POC Engagement Options by Starting Point
Starting Point | Engagement |
Technical idea | Identify and validate the core technical assumption |
Product requirements | Investigate the single highest-risk component |
Architecture proposal | Build and evaluate the proposed approach |
AI concept | Test the specific AI workflow |
Existing prototype | Validate the underlying technology behind a validated experience |
Existing application | Test a new technical capability before adding it |
Competing approaches | Build parallel experiments and compare results directly |
Why Build Your POC With Codersarts
Focused technical validation — we concentrate on the specific uncertainty that needs resolving, not a broad exploration.
Real engineering expertise — POCs draw on the same senior engineering bench used across application development, APIs, data processing, AI, and cloud infrastructure.
Evidence-based decisions — the deliverable is a documented recommendation, not just working experimental code.
Clear path forward — a validated POC feeds directly into MVP scoping, and a successful product's later production hardening runs through Product Engineering.
Frequently Asked Questions
What is a Proof of Concept?
A focused technical implementation built to determine whether a specific technology, integration, or approach is feasible — before committing to full product development.
What's the difference between a POC and an MVP?
A POC answers "can we build this technically?" An MVP answers "will users use and value this product?" They test fundamentally different risks.
Is a POC production-ready?
No — a POC is optimized for fast technical learning, not reliability or scale. Production requirements get addressed separately, typically through Product Engineering.
Can you build an AI POC?
Yes — LLM evaluation, RAG experiments, agent workflows, document processing, classification, and ML model validation are all common AI POC scopes.
Can an existing prototype become a POC?
Yes — if the prototype validates the experience but an underlying technical assumption remains uncertain, a focused POC validates that specific piece.
Can a POC become an MVP?
Yes, though POC code often needs partial rebuilding for production quality — the POC informs the MVP's architecture rather than becoming its final codebase.
How much does POC development cost?
Fixed-price POCs typically range from $2,000 for a focused API/integration test to $8,000 for a complex data or architecture experiment, depending on technical complexity and evaluation criteria.
How long does it take?
Most focused POCs run 1–2 weeks; complex AI, data, or architecture experiments can run 2–4 weeks depending on scope.
Validate Before You Build
Have a technical uncertainty that could affect your product? Let's build a focused Proof of Concept and get evidence before you make a larger development commitment.