Real architecture
Patterns that reflect how the system would actually be built, not disposable scaffolding, so the evaluation reflects reality.
AI Accelerator
AI Prototyping
Well-engineered proofs-of-concept that answer a clear question, not throwaway demos.
Most AI prototypes don't answer the question they were meant to answer, because most prototypes were never built to answer a question in the first place. Spruce's AI Prototyping accelerator is different by design. In six to ten weeks, we build a working proof-of-concept with measurable success criteria agreed up front, solid engineering so the result is trustworthy, and a production specification that scopes the separate implementation work required to take the use case into production. AI Prototyping is a feasibility and evaluation engagement. It is not an implementation engagement. Productionalization is a distinct step, delivered through AI Solutions Development or the Full AI Implementation accelerator, and scoped separately once you have the PoC in hand.
Well-engineered, not a throwaway demo
The word 'prototype' has been abused. Many so-called AI prototypes are demo scripts held together with hard-coded data and no tests, which makes their results impossible to trust and impossible to reason about at scale. A Spruce PoC is built to be evaluated honestly, not demoed theatrically:
Real testing
Unit, integration, and (where appropriate) evaluation harnesses for model behavior, so measured results are defensible.
Real data
Integration with representative source data your production version would rely on, under appropriate access controls, so the PoC answers the question against real-world signal rather than synthetic stand-ins.
Real observability
Logging, monitoring, and tracing so we can measure against success criteria and debug behaviour during the engagement.
Real security
Role-based access, encryption, audit logging, and compliance fit for your regulatory environment from the first commit.
This engineering bar is what makes the PoC a credible evaluation artifact. It is not a claim that the PoC is production-ready. Moving a PoC into production requires the separate hardening, scale, integration, and operations work handled by AI Solutions Development or Full AI Implementation.
Scope and success criteria
Every AI Prototyping engagement starts with a tight definition of scope and measurable success criteria. We don't build a prototype without knowing how we (and you) will decide whether it worked. Success criteria are tied to the business outcome the use case is meant to achieve, not just to model-level metrics, and they are agreed by your sponsor before development begins. That alignment is what makes the go/no-go decision defensible at the end of the engagement.
The three-phase process
AI Prototyping runs six to ten weeks across three phases. The middle phase is where most of the engineering happens; the first and third phases are where scope, risk, and path-to-production are nailed down:
Phase 01
Planning and Requirements (2 to 3 weeks)
Use case definition, success criteria, data access and integration planning, model selection, architecture design, and risk and compliance review.
Phase 02
Model Development and Data Preparation (2 to 4 weeks)
Data pipeline build, model selection and (where required) fine-tuning, application layer build, integration with source systems, and iterative testing against success criteria.
Phase 03
Testing, Evaluation, and Path to Production (2 to 3 weeks)
Structured evaluation against success criteria, user-experience validation with a pilot cohort where in scope, production specification drafting, and rough-order-of-magnitude estimate for the full build.
Typical prototype types
The AI Prototyping accelerator covers the use case patterns most commonly pursued by our clients. Each of these has an established architecture and model pattern at Spruce, which is how we can deliver a well-engineered, defensibly-evaluated PoC in six to ten weeks:
- Document classification and extraction — structured data pulled from forms, filings, and correspondence.
- Generative knowledge assistants and copilots — grounded in your policies, procedures, and institutional knowledge.
- Retrieval-augmented question answering (RAG) — over the content and data sources your users actually need.
- Predictive analytics and operational forecasting — service demand, resource allocation, anomaly prediction.
- Intelligent search and summarization — over legal, research, clinical, or archival content.
- Conversational AI — constituent, customer, or employee-facing voice and chat assistants.
- Fraud and anomaly detection — across claims, benefits, and transactional data.
Model selection methodology
Model selection is part of the engagement, not a default. We evaluate candidates across commercial models (Azure OpenAI, OpenAI direct, Anthropic Claude, Google Gemini, Cohere), managed platform models (Azure AI Foundry, AWS Bedrock, Google Vertex AI), open-source models (Llama, Mistral, Qwen, and domain-specific fine-tunes), and classical ML approaches where they fit better than a large language model. We document the selection rationale and design the system so the model can be swapped if a better option emerges. Model selection is never locked to a single vendor or a single model provider.
What happens after the PoC
AI Prototyping ends with a working proof-of-concept, a clear evaluation against the success criteria agreed at the start, and a go/no-go recommendation you can take to a sponsor or funding body. Productionalization is the next, distinct step. It is not bundled into AI Prototyping, because the engineering, integration, operations, and governance work required to run a system in production is substantially larger than the work required to prove feasibility.
If the go decision is made, clients typically move into one of two Spruce engagements to take the use case into production:
- AI Solutions Development — the right path when productionalizing this use case is part of an ongoing AI engineering capability, a portfolio of solutions, or a long-term partnership. Best for clients who expect multiple production systems over time.
- Full AI Implementation — the right path when the client wants a packaged, scoped 4-to-12-month program to take this specific use case from specification through production deployment and early operation.
Production specification as the handoff
The production specification we deliver is written so either engagement can pick it up as a starting point, with the remaining work (hardening, scale, integrations, SLAs, and operations) enumerated and estimated. Clients who want to take the specification to an internal team or another vendor can do that too; the specification is deliberately platform- and team-neutral. Either way, the PoC codebase and specification give the implementation team a material head start, and a number of engineering decisions made during the PoC will carry through. We will not, however, claim the PoC is itself production-ready.
Deliverables
- Working proof-of-concept — deployed to an environment your team can access, with documentation and test coverage.
- PoC summary report — structured evaluation against the agreed success criteria, including qualitative feedback from pilot users where in scope.
- Production specification — architecture, integrations, data, security, observability, and remaining work to reach production readiness.
- Model selection rationale — documented evaluation of candidate models against the use case.
- Rough-order-of-magnitude cost and timeline estimate — suitable for budgetary approval, SOW, or RFP.
- Go/no-go recommendation — our honest recommendation on whether to proceed, with the reasoning your sponsor needs to make the call.
Best for
- Organizations that have identified a top-priority use case (through an AI Roadmap engagement or otherwise) and want to validate it before committing to a production build.
- Leaders who need measurable proof to unlock budget for full implementation.
- Teams that have been burned by prototypes that didn't survive productionization and want a different approach.
- Agencies and enterprises that need a defensible, evidence-based basis for vendor selection or funding decisions.
- Innovation and AI teams that want to de-risk an ambitious use case before proposing it at the executive level.
Who you'll work with
AI Prototyping engagements are delivered by a cross-functional Spruce team: a solution architect, one or two AI-supervising engineers, a data engineer, and a UX designer for use cases with a user-facing surface. A lead advisor stays involved to own success criteria and the production specification. Specialists (security, compliance, domain) are added where the use case calls for it.
Ready to move forward?
Every Spruce engagement begins with a short conversation about your goals, constraints, and timeline.