Greenfield AI products
Mobile and web products where AI is the experience: recommendation engines, generative interfaces, conversational products, multi-agent applications.
Service
AI Product & Platform Engineering
Production AI products and SaaS platforms, built to scale.
Greenfield AI products and SaaS platforms with AI built into the architecture from day one: observability, fallback paths, eval frameworks, multi-tenancy. Built by senior engineers who've shipped at venture scale.
KUMO's Anthropic Claude engineering →KUMO's OpenAI development →
Where we focus.
Six application areas where this service ships measurable results, chosen against the failure modes most growing businesses hit.
SaaS platforms
Multi-tenant SaaS with AI integrated where it adds value. Rails, Node, Python, Go backends with React or Next.js frontends. Cloud-flexible deployment.
Vertical AI platforms
Domain-specific AI products for fintech, healthcare, legal, real estate, built with the industry's data shapes, compliance, and integrations in mind.
Multi-agent applications
Coordinated AI agents that plan, retrieve, write, and verify across systems. Built with explicit handoff points, structured outputs, and per-agent eval suites.
AI-first data platforms
Data lakes, vector stores, semantic search, and feature pipelines designed for AI workloads from the start, not retrofitted into a traditional warehouse.
Multi-tenant AI SaaS
Per-tenant isolation for prompts, data, and evals. Usage-based pricing instrumentation, customer-level model routing, and tenant-aware audit trails.
Reality check
Why most AI products die between the demo and the launch
Roughly 88% of enterprise AI pilots never reach production, and MIT's research found 95% of generative AI pilots deliver no measurable financial impact. The demos were not the problem. The demos were good.
What kills them is that a demo and a product are different objects. A demo answers the happy path on clean data for one user. A product handles the ambiguous input, the empty state, the model outage, the customer whose data breaks an assumption, and the invoice at the end of the month. Those are architecture decisions, and they are cheap on day one and expensive in month six.
The decisions that have to be made before the first line of code: where evaluation runs and what it blocks, what happens when the model is unavailable or unsure, how tenants are isolated, how token cost is attributed per customer, and whether you can change model provider later without rewriting the product. Get those right and the product scales. Retrofit them and you rebuild.
Related work
Where we've shipped this in production.
FINTECH · YC S20 · $31M+ raised Volopay
First production version of a YC S20 fintech operating across six countries. Engineering partner through scale.
Read case study -> B2B PROCUREMENT · MULTI-AGENTRacepoint
Multi-agent LLM SaaS that collapses B2B procurement into a natural-language workflow.
Read case study -> EVENT DISCOVERY · UKSumwhere
Greenfield event-discovery product built end-to-end: discovery, scoping, build, and launch.
Read case study ->
AI AUDIO · CONSUMER · EUROPE Kaft
How KUMO designed and built the AI audio system behind Kaft, a personalized AI news podcast that turns a listener's interests into conversational two-host episodes.
Read case study ->What it costs
What this costs
The short answer
A first production AI product at KUMO runs $20K to $50K over 4 to 16 weeks: one real product, shipped, with evaluation and monitoring built in rather than bolted on. Multi-feature platforms run $50K to $100K over 16 to 24 weeks.
Starter Build
$20K to $50K
4 to 16 weeks
A first production build, shipped end to end with evaluation and human review built in.
Grow Build
$50K to $100K
16 to 24 weeks
Multi-workflow or multi-feature platform work, re-engineered for scale.
Support and Growth Team
$5K to $10K per month
Ongoing, cancel with 30 days notice
The engineering team on retainer once it is live: new work, evals, and monitoring.
These are our three live engagement bands, the same across every KUMO service. Not sure which one fits? That is exactly what the first call is for.
Technologies and platforms
Assembled for your build.
FAQ
How is this different from adding AI to an existing product? +
If you have an existing product, see AI Integration. This service is for greenfield builds: new products where AI is central to the architecture, not retrofitted. Different infrastructure decisions from day one.
What's the typical timeline from idea to launch? +
First production version in 12-16 weeks. Larger products with multiple AI features run 4-6 months. Working prototype in week 4-6.
Will you tell us if our idea won't work? +
First milestone is a scoping deliverable that pressure-tests the idea. If something looks off, we tell you in week 2 rather than week 12.
Who owns the IP and AI assets? +
You do. All code, prompts, evals, fine-tuned models, and configurations belong to you on milestone payment.
What does a typical build engagement look like? +
Scoping in week 1-2, working prototype by week 4-6, V1 to production in 12-16 weeks. Larger multi-feature builds run 4-6 months across 2-3 production releases. Milestone-based with go/no-go reviews at each step, you keep the option to stop or pivot.
How much does it cost to build an AI product? +
A first production version runs $20K to $50K over 4 to 16 weeks. Multi-feature platforms run $50K to $100K over 16 to 24 weeks. Ongoing engineering is $5K to $10K per month.
What makes an AI product different from normal software? +
Non-determinism. The same input can produce different output, so quality has to be measured continuously rather than proven once, which means evaluation harnesses, fallback paths, and human checkpoints are architecture, not features.
Can you build multi-agent systems, or is that hype? +
Both. Multi-agent architectures earn their place when a task genuinely decomposes into steps needing different tools and verification. They are also frequently over-applied to problems one well-designed call would solve. We will tell you which you have.
Will we be locked to one AI model provider? +
No. Models sit behind a provider-swappable abstraction, so changing provider is a configuration decision rather than a rewrite.
Tell us what you're solving for.
We'll listen first, ask the right questions, and follow up with a clear proposal.