Use-Case and Success Criteria Definition
We clarify the problem, the decision the PoC must inform, the constraints, and the measurable threshold that counts as success.
AI Proof of Concept Services
Validate Your AI Initiative Before You Scale
We build focused, working AI proofs of concept on your data and on AWS, so you can judge technical feasibility, business value, and risk before committing to full-scale development.
- Working software, not slideware
- Built on your real data
- Designed to evolve into production
Why a PoC
Answer the risky question first, while it is still cheap to change course
Most AI initiatives carry one or two assumptions that decide whether the whole investment makes sense: whether the data is good enough, whether the model can reach the accuracy the workflow needs, or whether the cost per transaction works at scale.
A proof of concept tests those assumptions directly, in a controlled, low-risk scope. Our PoCs are fully functional and cloud-deployed, so the result is evidence your team and stakeholders can inspect rather than a presentation about what might be possible.
Scoped to a decision
Every PoC ends with a clear go, adjust, or stop recommendation
We agree on success criteria before the build: the metric, the threshold, and the data the result must hold up on. At the end you get the working PoC, the evaluation results, known limitations, and a production cost and architecture outline, so the next decision is grounded in evidence.
Beyond prototypes and slideware
Each PoC is the smallest meaningful implementation that can validate the assumptions behind your initiative, built so the parts that work do not have to be thrown away.
Data Feasibility Review
An early check of the data you have, its quality and coverage, and what it can realistically support before engineering effort is spent.
Cloud-Native Build on AWS
Managed compute, secure storage, data pipelines, and AI/ML tooling following AWS best practices for performance, reliability, and security.
Evaluation on Real Inputs
A test set drawn from your data and the cases that matter, with results reported against the success criteria agreed at the start.
Security and Compliance from Day One
Access controls and data handling designed for regulated sectors, including healthcare, insurance, payments, and finance.
Scale-Ready Foundation
An architecture designed to evolve into a production system if validated, without re-architecting or starting from scratch.
What happens after the PoCExample engagement
An AI-assisted identity verification and KYC prototype
For an identity verification platform, we built an AI-assisted prototype for KYC workflows that structures customer information and supports faster compliance review, connected to backend APIs and the surrounding application.
See more of the work we have delivered- Docs
- AI-assisted identity and document analysis
- KYC
- compliance workflow automation
- APIs
- backend and application integration
How we deliver an AI proof of concept
- 01
Discovery and Use-Case Definition
We clarify the problem, success metrics, constraints, and business goals so the PoC targets real value rather than experimentation for its own sake.
- 02
Right-Sized Design
We define the smallest meaningful AI implementation that can validate the key assumptions, reduce risk, and demonstrate impact.
- 03
Implementation and Iteration
Our engineers build the PoC on secure, modern architecture, using real data, domain context, and explainability where it is required.
- 04
Validation and Feedback
We test performance against the agreed criteria, gather stakeholder feedback, and evaluate business outcomes to inform the next step.
- 05
Scale-Ready Handoff
You receive the PoC, the results, and a production outline. If the PoC is validated, it can evolve into a production-ready system.
Who it is for
From AI ambition to AI reality
Startups validating a new AI-driven product or feature before scaling. Enterprises exploring AI adoption without disrupting core systems. Innovation teams testing high-impact use cases with real data and domain context. Founders and executives preparing for investor or stakeholder buy-in with a working demonstration.
We work across healthcare, insurance, payments, finance, and other regulated sectors where correctness, transparency, and trust matter.
What teams ask before we start
Clear answers on scope, architecture, data, and delivery.
What is an AI proof of concept?
An AI proof of concept is a small, working implementation that tests whether a specific AI approach can deliver the required result on your data, under your constraints. It answers a feasibility question before you commit to a full build.
How is a PoC different from a prototype or an MVP?
A PoC tests whether something can work technically. A prototype explores how it should look and behave for users. An MVP is the smallest version of the product released to real users. A PoC usually comes first when the main risk is technical feasibility.
Do your PoCs run on real data?
Yes, wherever possible. A PoC that only works on clean sample data proves very little, so we evaluate against data that reflects the conditions the system will face in production.
What do we receive at the end of a PoC?
A working, cloud-deployed PoC, evaluation results against the agreed success criteria, known limitations, and an outline of the production architecture and cost so you can decide whether to proceed.
Can a PoC become the production system?
Our PoCs are architected on AWS best practices so a validated PoC can evolve into production without starting from scratch. Production still adds work such as hardened data pipelines, integration, monitoring, and security reviews.
Can we buy a PoC through AWS Marketplace?
Software Sushi lists AWS professional services on AWS Marketplace, including proof of concept engagements. See our AWS Marketplace page for current listings.
How much does an AI proof of concept cost?
Cost depends on the use case, data readiness, and integrations involved. We scope the PoC to the decision it must inform and provide a clear estimate after discovery.
Related expertise
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Your initiative
Book a Discovery CallReady to validate your AI initiative?
Tell us about your use case and we will scope a PoC that tests the assumptions that matter and demonstrates real value.