SSI builds a focused proof of concept for one use case.
We test it on your data, measure the results in your
workflow, and give you the evidence to decide
what comes next.
AI Proof of Value is a short, focused engagement for one defined use case. SSI builds a working proof of concept that shows whether AI can do the job. It runs on representative data, under conditions that reflect your workflow.
We measure what matters to the decision. Is the output accurate enough to trust? Does it save time for the people doing the work? What will it cost to run? Every answer is compared against an agreed baseline. You leave with clear evidence and an honest recommendation on whether to proceed.
Focused proofs of concept across the AI capabilities enterprises use most. Each one targets a single task and clear success criteria.
Test an assistant or content workflow built on selected language models. We measure output quality and how much review the results still need.
Test an agent that uses selected tools to complete a bounded task. We measure task completion, failure handling, and where people need to step in.
Connect a sample of your documents and test grounded answers. We measure retrieval relevance, answer accuracy, and source citations.
Train and compare models for a defined prediction or classification task. We test performance on holdout data the model has never seen.
Test image or video models on a targeted detection or inspection task. We measure missed detections, false alarms, and processing speed.
Automate one recurring process in a controlled setting. We measure turnaround time, manual effort saved, and how exceptions are handled.
Everything needed to prove the concept. Nothing that belongs in a production build.
The right approach for the task, agreed before the build begins.
Agree the task, the users, and the current baseline. Set the success criteria the concept must meet.
Compare suitable models and tools against the task and budget. Record each choice and the reason behind it.
Every engagement ends with three clear outputs.
A functioning demonstration of the AI on your data.
Benchmarks, success criteria, and documented failures.
Proceed, adjust, or stop. With the reasons behind it.
Short, focused iterations. Each cycle brings the concept closer to a clear answer.
Agree the use case, the baseline, and the success criteria. Confirm the data and access the test needs.
Set up a secure environment and a representative data sample. Connect sandboxes or extracts where required.
Develop the core AI capability. Review outputs with your domain experts as the work progresses.
Run the evaluation and inspect failure cases. Benchmark quality, speed, and cost against the baseline.
Demonstrate the working concept and share the results. Recommend whether to proceed, adjust, or stop.
A proof of concept answers one question. Will it work? When the answer is yes, the production build is scoped as a separate phase.
Delivery teams
Human-AI hybrid teams.
Smart Pods take the proven approach into implementation. A cross-functional team brings engineering, design, and QA together. They integrate the solution, test it, and prepare it for release.
Reusable platform
AI-first, enterprise-ready platform
Momentum, SSI’s enterprise delivery framework powered by AI, provides reusable microservices and agentic skills. The findings guide what to reuse, adapt, or develop for production.
Discuss your requirements with our team.
Bring a defined use case or an AI idea you want to test. We will scope the proof of concept and the measures that will show its value.
