AI-Driven Product Discovery
Two weeks to answer “should we build this, and what would it actually cost to run?” — before you spend six months and a budget finding out. Run by senior AI engineers. Accelerated by the same AI tools we use to ship in production.
Who this is for
You have a vision but no architecture
Leadership has agreed AI matters. Engineering can’t quote a timeline because failure modes, eval criteria, and cost-per-request are unknown. Discovery turns the vision into a buildable spec.
You’ve prototyped and you’re stuck
A vibe-coded demo or notebook that works in the room and falls apart everywhere else. We tell you whether to harden, restart, or shelve.
You’re evaluating vendors or build partners
You need an independent technical position before handing a six-figure budget to an agency. Discovery produces the brief that lets you compare proposals on substance, not slide decks.
You’re entering regulated territory
Healthcare, fintech, legal — anywhere a wrong AI output has real downside. Discovery includes policy gates, audit logging, and human-in-the-loop design before a single line of production code ships.
What you get
Six deliverables. Every one of them yours to keep.
1. Evaluation set (your AI spec)
A dataset of real inputs paired with reference outputs and assertions. Easy cases, edge cases, adversarial inputs. The spec your AI must meet — reusable across model swaps and vendor changes.
2. Architecture proposal
Working architecture diagram with the simplest viable design. Data flow, tool inventory, failure modes, and written rationale for every non-obvious choice. Multi-agent only where the eval forces it.
3. Cost-per-request model
The actual dollar cost at 1x, 10x, and 100x your projected traffic. Token math, cache assumptions, routing strategy, and the cost levers you can pull when the bill grows. The run cost you’ll live with for years — not the build cost.
4. Build estimate with confidence intervals
Engineer-month estimate broken down by component, with assumptions stated. P50 and P90 timelines. Where the risk lives.
5. Go/no-go memo
A written recommendation — build, shelve, or restart — in three paragraphs your board can read in five minutes. We’ve recommended “don’t build this” before; we’ll do it again if the numbers say so.
6. Risk & compliance register (regulated workloads)
Data residency posture, audit logging design, fairness and drift considerations, the policy gates that need deterministic code instead of prompts. Drafted to be defensible under examination.
What we use during Discovery
Eval & testing
Custom eval harnesses
LangSmith
Helicone
Promptfoo
ragas
Models
OpenAI
Anthropic
Azure OpenAI
Gemini
Llama
Mistral
DeepSeek
Qwen
open-weight
Frameworks
LangChain
LangGraph
LlamaIndex
MCP
Function Calling
Retrieval
Pinecone
Weaviate
pgvector
Qdrant
AI-accelerated work
Claude Code
Cursor
internal prompt-management
automated competitive scanning
How a Discovery sprint runs
Two weeks, four stops — from open question to a defensible decision.
Discovery is the expanded “Discover” stage of our Production-Ready AI Engineering SDLC — the same method we run on every build, focused into a fixed two-week engagement that answers should we build this, and what would it cost to run? before you commit.
- 01Days 1–2
Problem framing
HumanMap input domain, output requirements, and failure cost. Interview stakeholders and end users. Pull together every existing artifact — prototypes, research, vendor decks. By end of Day 2 we know what "done" means in measurable terms.
- 02Days 3–7
Eval set + architecture sketch
Human + AIBuild the first eval set from real anonymized inputs. In parallel, sketch the simplest architecture that could meet the spec, prototype the critical path, and run the eval. We deliberately try to break the design before we trust it. By end of Day 7 we have numbers, not opinions.
- 03Days 8–10
Cost model & risk pass
AIToken math, latency profiling, cache and routing analysis, multi-tenant and scale-up projections. For regulated workloads, the compliance and audit-logging pass runs here. We meet with finance, legal, or risk stakeholders if relevant.
- 04Days 11–14
Decision packet
HumanAssemble deliverables, write the go/no-go memo, and present everything in a 60-minute working session with your team. You leave with a tested architecture, real numbers, a defensible recommendation, and a clear path to build — with us or anyone else.
Run a 2-week Discovery instead of a 6-month bet.
Most AI roadmaps slip because the team built before they knew what “done” meant. Discovery answers that question first.
Development Formats and Billing
Discovery sprint
Two weeks, fixed scope. Quoted after a free 30-minute scoping call. Most engagements land in a defined range that’s a fraction of even a small build phase. You own all deliverables — no exclusivity clause, no internal “best practices library” we retain rights over.
Phased contract — Discovery first, build second
A phased contract protects both sides. We get to validate the work is real. You get to validate we’re the right partner. If Discovery green-lights the build and you continue with us, we move directly into production engineering on Monthly Team Allocation, starting from a much stronger position than a build that skipped Discovery.
Free 30-minute architecture review
Before any paid engagement, we’ll spend 30 minutes pressure-testing your AI architecture or vendor proposal — no slides, no pitch. Often that’s enough to surface the next move.
We offer 2 approaches
to developing your project
Discovery
only
A fixed two-week sprint that ends with deliverables you own outright — eval set, architecture, cost model, go/no-go. Take them to any build partner, including your own team.
Why a Discovery sprint, not a build proposal?
Most AI agencies skip Discovery and quote you a six-month build. Here’s why we don’t.
Because the eval is the spec
The single best predictor of whether an AI build ships is whether the team built the eval set before the prompt. The eval is the spec; the prompt is the implementation. Sequencing matters.
Because the cost-per-request is the contract
Build cost is a one-time number. Per-request cost is what you live with for years. Discovery puts a defensible number on it before anyone signs a build SOW.
Because the right answer might be “don’t build this”
Some AI ideas don’t survive contact with their own eval set. Some look great until cost-at-scale gets modeled. We’d rather tell you that in week two than month six. Roughly 1 in 8 Discovery engagements ends in a no-build recommendation. The fee is paid either way — it’s the cheapest possible insurance against committing six months and a budget to the wrong thing.
Because either party should be able to walk
A phased contract — Discovery first, build second, contingent on Discovery’s conclusion — protects both sides. Nobody is committed beyond what’s been earned. This is how serious work gets done.
Why teams choose Diffco for Discovery
Senior AI engineers in the room
The team in the room with you is the same team that would build the product. Engineers who have shipped LLM-powered systems to production traffic, debugged prompt regressions, designed cost-controlled inference layers, and rebuilt vibe-coded prototypes into systems that compound.
C-level & VP access throughout
Discovery is product-heavy, not just technical. Diffco’s CTO and VPs are available throughout the sprint for product strategy, architecture trade-offs, GTM framing, and the calls that need a senior voice in the room — not a junior consultant relaying notes.
18 years of production software depth
Most AI failures are software engineering failures. We’ve debugged race conditions, written migrations against 50M-row tables, and operated systems through real incidents. AI is the new layer; the engineering underneath is what makes it hold up.
AI-accelerated process
Claude Code, Cursor, internal prompt-management, automated competitive scanning. What used to take six weeks of consultant time takes two weeks of senior engineering time. The acceleration is real, and you see it in the depth of deliverables.
Domain depth
Fintech, healthcare, B2B SaaS, marketplaces, regulated workflows. For sensitive domains, we bring the compliance and audit posture from day one — not retrofitted at the end.
Falsifiable deliverables
Eval sets, architectures, and cost models you can verify. No “internal best practices library” we retain rights over.
Independent perspective
We’ll recommend not to build if that’s the right answer. We ship the work — we don’t just spec it.
Success stories of our clients
We set up AI-native development across RedTrack's ad-tracking platform
We built an AI moderator that runs automated user research interviews at scale
Full development + AI agentic product discovery
We set up AI-native development across RedTrack's ad-tracking platform
We built an AI moderator that runs automated user research interviews at scale
Frequently Asked Questions
Frequently Asked Questions
How long is a Discovery sprint and what does it cost?
Two weeks of calendar time, fixed scope. Fee depends on the complexity of the domain (regulated workloads cost more than greenfield consumer features) and the size of the input corpus. Quoted after a free 30-minute scoping call.
Who owns the deliverables?
You do, in full. Eval set, architecture, cost model, go/no-go memo — all yours. No exclusivity clause, no lock-in. If you take the deliverables to another build partner, that’s a perfectly fine outcome.
What if Discovery concludes we shouldn’t build the thing?
We say so, in writing, with the reasoning. Roughly 1 in 8 Discovery engagements ends in a no-build recommendation. The fee is paid either way — and in our view, it’s the cheapest possible insurance against committing six months and a budget to the wrong thing.
Can Discovery roll into a build phase with Diffco?
Yes — that’s the most common path. If Discovery green-lights the build and you continue with us, we move directly into production engineering with the architecture, eval harness, and cost model already defined.
Show more answers
Get a senior engineer’s read on your AI plan.
Even before a paid Discovery sprint, we’re happy to spend 30 minutes pressure-testing your architecture or vendor proposal — no slides, no pitch.
Let’s build something
great together.
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