AI agents for a specific workflow
Intake, research, preparation or routing tasks that use your tools and data with human approval.
AI Proof of Concept Development
AI Proof of Concept: Prove It Works Before You Fund the Build
In about four weeks we turn one high-value AI use case into a working prototype on your real data, measure how well it performs, and give you an architecture and estimate for production, so you can decide with evidence instead of a demo.
What Is This Service?
Why start with a proof of concept
Most AI initiatives do not fail because the model is weak. They fail because the use case was vague, the data was not ready, nobody measured quality, or the prototype could not survive real users and systems. A focused proof of concept answers the questions that matter before you commit a full budget: does it work on our data, how accurate is it, what will it cost to run, and what does production require?
Our proof-of-concept engagement is deliberately narrow: one workflow, representative data, explicit success criteria and an evaluation you can trust. You keep the prototype, the evaluation set and the findings whether or not you continue with us.
Features & Capabilities
Good candidates for an AI proof of concept
RAG assistants
Answers with citations from your documents, policies, tickets or knowledge base.
Document extraction
Pulling structured fields from PDFs, forms, emails or scans into your systems.
Clinical documentation
Transcription and structured note generation for a defined specialty or role.
Customer or employee chatbots
Support assistants grounded in your content with escalation to people.
Prediction and classification
Forecasting, risk scoring or triage models on your historical data.
Business Benefits
Business value designed into the system
Evidence, not opinions
Success criteria are agreed up front and measured on your data.
Contained risk
A short, fixed-scope engagement instead of an open-ended build.
A clear path to production
Architecture, integrations, security considerations, running costs and an estimate for the full build.
Reusable assets
The prototype, evaluation set and findings are yours to keep.
The Four Weeks
What happens in a typical proof of concept
Week 1 — Use case and success criteria
We define the workflow, users, data access, constraints and the measurable results that would justify a production build.
Week 1–2 — Data and evaluation set
We gather representative, appropriately protected data and build an evaluation set that reflects real cases, including difficult ones.
Week 2–3 — Prototype
We compare suitable models and approaches and build a working prototype with a simple interface your team can try.
Week 3–4 — Evaluate and iterate
We measure quality, latency and cost against the success criteria and improve the weakest points.
Week 4 — Readout
We present results, limitations, production architecture, risks and an estimate, and hand over everything we built.
Next step — Your decision
Move to an MVP, adjust the use case, or stop with clear evidence. All are good outcomes.
Technologies We Use
A production stack selected for your requirements
We select the simplest dependable approach for the use case and compare providers on your data.
- OpenAI
- Anthropic
- LangChain
- LlamaIndex
- Python
- FastAPI
- Vector Databases
- PostgreSQL
- AssemblyAI
- Whisper
- PyTorch
- AWS
- Azure
Who It's For
Built for teams like yours
Healthcare and health-tech
Documentation, intake, administrative automation and patient-record Q&A.
SaaS and software companies
AI features inside an existing product.
Operations-heavy businesses
Document processing, routing and internal knowledge assistants.
Construction and field services
Field data, site documentation and project records.
Why Torch Solutions
Why run your proof of concept with us
We have shipped AI to production
Our work includes an AI clinical documentation platform that cut documentation from more than an hour to about 20 minutes per patient.
Evaluation first
We measure quality with your data and agreed criteria, not a handful of cherry-picked examples.
Production-minded prototypes
We design with integrations, security, cost and monitoring in mind so the path to production is real.
Honest recommendations
If the use case is not ready or AI is the wrong tool, we will tell you.
Related Case Studies
From proof to production
SureScribe AI Clinical Documentation Platform
HIPAA-aware AI scribe: clinical speech recognition, multi-stage LLM notes, clinician approval and Athenahealth / CharmHealth integration. Documentation cut from 60+ to about 20 minutes per patient.
Read Case Study →
WebGIS 3D Construction Site Scanning Platform
iPhone LiDAR, GPS and photogrammetry capture turned into point clouds, USDZ/GLB models, GeoTIFF overlays and site measurements in a web dashboard.
Read Case Study →
HomeTeams AI Elderly Care Platform
Caregiving app with voice-based conversational AI, care plans, task coordination, secure team chat and accessible design for older users.
Read Case Study →What You Get
One use case, about four weeks, a clear decision
The proof of concept is fixed in scope and agreed in writing after a free 30-minute scoping call.
Free scoping call
30 minutes, no cost
Discuss the use case, data and constraints, and whether a proof of concept is the right next step.
- Use-case fit
- Data and access questions
- Proposed scope
Proof of concept
About 4 weeks
A working prototype on representative data, evaluated against agreed criteria.
- Working prototype
- Evaluation set and results
- Cost and latency measurements
Production plan
Included in the readout
Everything you need to decide on and budget the full build.
- Production architecture
- Risks and security considerations
- Estimate and roadmap
If you continue, the proof of concept becomes the foundation of the MVP; a focused MVP typically takes four to eight weeks.
Related Services
Combine this capability with the application, cloud, data, integration, and product engineering required to operate it reliably.
Frequently Asked Questions
Questions about AI Proof of Concept
What is an AI proof of concept?
A short, focused project that builds a working prototype for one AI use case on representative data and measures whether it meets agreed success criteria, so you can decide whether to invest in a full build.
How is a proof of concept different from an MVP?
A proof of concept answers 'can this work well enough?' with a prototype and evaluation. An MVP is production software real users rely on, with integrations, security, monitoring and support.
How long does it take?
About four weeks for a single, well-defined use case. Timing depends mainly on how quickly representative data and system access are available.
What do we need to provide?
A business owner for the use case, access to representative data or documents, a few users to try the prototype, and agreement on what success looks like.
Can you work with sensitive or healthcare data?
Yes. We work under NDAs and plan healthcare projects around HIPAA-aware architecture, minimizing the data used and routing PHI only to appropriately covered services.
What happens after the proof of concept?
You receive the prototype, evaluation results, a production architecture and an estimate. You can continue to an MVP with us, take the findings in-house, or stop with clear evidence.
Have an AI idea you want to validate? Contact Torch Solutions.
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Ready to Solve the Right Software Problem?
Talk with an experienced software team about your goals, workflows, users, integrations, and technical risks before you commit to a roadmap, architecture, or development budget.