The problem is not just risk. Most AI proofs of concept are built as technical experiments, not business validation tools. They answer “can we build it?” but not “is it worth building?” Without a clear business case designed in from the start, with defined success metrics, financial benchmarks, and a projected return, the prototype has no way to tell you whether it justifies investment. A go/no-go decision needs numbers, not just working code.
Symphony designs every AI proof of concept with the business case built in from the start. Before a line of code is written, we agree the financial benchmarks, success criteria, and go/no-go thresholds the PoC will be measured against. The prototype is then built and tested on your real data with those targets in view, so the output is a projected ROI, an implementation cost estimate for the next phase, and the evidence for a funded decision. Fixed scope, fixed price, 2–4 weeks.
Everything from use-case scoping and data readiness assessment to prototype build, evaluation, and an ROI business case ready for budget sign-off.
Every engagement starts with a defined scope. We work with you to frame the business problem, agree on measurable success criteria and the financial benchmarks your ROI calculation will be built from, and lock the go/no-go thresholds before any technical work begins. The output is a fixed scope that governs the entire PoC and gives your stakeholders a clear standard against which the final business case will be measured.
Before the build starts, you get a structured assessment of your available data: its quality, completeness, and suitability for the AI approach in scope. We map integration points, flag compliance and security considerations, and identify any gaps that need addressing before development begins. The result is an honest feasibility view based on your real assets, so you commit to the prototype on solid ground, not on assumptions.
You receive a clear recommendation on which AI approach fits your use case: RAG, fine-tuning, a hybrid architecture, or a pre-built API integration, together with the tooling, data pipeline, and integration pattern required. This is a concrete architectural decision, not a shortlist of options. It resolves the choices that most commonly cause rework in a full build and ensures the technical decisions made in the PoC carry directly into production.
You get a working prototype built on your real data and business workflows, not a sanitized demo dataset. We test for accuracy, latency, and reliability under realistic operating conditions, benchmarking results against the KPIs defined in scoping. This is where the PoC answers the question that matters: does this approach actually work, with your data, in your context?
A PoC without a business case is just a demo. The PoC concludes with a projected ROI calculation for the next phase: estimated implementation cost, expected financial returns, and a business case ready for budget approval. Combined with benchmark results against your agreed KPIs and a model and architecture recommendation, this gives your decision-makers a complete, evidence-based go/no-go package. The ROI calculation is not an appendix to the PoC. It is the reason you run one.
From problem alignment and feasibility through prototype build and evaluation to a go/no-go decision and production roadmap.
From LLM and agentic prototypes to predictive models, computer vision, conversational AI, and rapid low- code builds.
The impact of a well-run AI PoC reaches well beyond the prototype. It shapes decisions, accelerates delivery, and builds organizational confidence in AI.
An AI proof of concept in healthcare looks nothing like one in fintech or logistics. We bring cross-industry experience to every PoC engagement, scoping and validating AI solutions that account for sector-specific data, compliance, and workflow constraints.
We offer the following development team extension models:
An AI proof of concept is a focused, time-boxed build that validates whether a specific AI approach is technically feasible and delivers measurable business value in your environment. Unlike a full implementation, a PoC is scoped to prove one thing: will this work, on your data, in your workflows, well enough to justify the investment? The output is a working prototype, benchmark results against agreed KPIs, and a projected ROI for the next phase, giving your stakeholders the evidence to make a confident go/no-go decision.
A prototype is an early-stage build used to explore a concept’s form and function, often without real data or production constraints. A PoC validates technical feasibility in a real environment against agreed success criteria. What most vendors call a proof of value, adding explicit business impact measurement, is built into every Symphony PoC as standard. The ROI calculation and business case are part of the core deliverable, not a separate engagement. An MVP is a different stage entirely: a minimum viable product released to real users to gather market feedback, which follows a successful go/no-go decision to proceed.
Running a proof of concept for AI takes 2–4 weeks at Symphony, at a fixed price agreed before work starts. The AI proof of concept deployment time depends on use-case complexity, data readiness, and the number of integration points involved, but the fixed-price model means scope, timeline, and cost are locked upfront with no runaway discovery costs. Low-code and no-code rapid prototypes are available on a shorter 2–5 day timeline for simpler use cases. Contact us to discuss your use case and receive a scoping estimate.
At the end of a Symphony AI proof of concept engagement, you receive a working prototype built and tested on your real data, benchmark results against the KPIs and financial targets agreed at scoping, and a model and architecture recommendation. You also receive a projected ROI calculation with an implementation cost estimate for the next phase and a phased production roadmap so the build that follows starts faster. Together, these give your decision-makers a complete evidence package for a confident go/no-go call.
The data requirements depend on the type of AI being validated. For generative AI and LLM PoCs, we typically need a representative sample of the documents, conversations, or content the system will work with. For predictive and machine learning PoCs, historical data covering the outcome you want to forecast or detect is required. For OCR and document processing PoCs, a representative sample of the document types, formats, and layouts the system will process is required, ideally including examples of edge cases and format variations. We assess data readiness as a formal step in the PoC process and will confirm exactly what is required, and whether any gaps need addressing, before the build starts.
The go/no-go decision is based on the success criteria and financial benchmarks agreed at the start of the engagement, not on subjective judgment. We measure the prototype’s results against those thresholds and deliver the findings as a structured evaluation: benchmark performance against agreed KPIs, a projected ROI calculation, and an implementation cost estimate. If the results meet the thresholds, the business case for proceeding is clear. If they do not, the PoC has done its job by preventing a larger investment in an approach that is not ready.
If the go/no-go evaluation confirms the approach is viable, the PoC findings translate directly into a production roadmap. This includes the architecture and integration decisions made during the PoC, a phased delivery plan, and the team composition and effort estimates needed to scope the full build. Because the architecture is already validated and the scoping work is done, the production build starts faster and with significantly less discovery overhead than a project that goes straight to implementation without a PoC.
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