Pre-product founders
You have funding and a vision. You need to ship a real AI solution to real users in months, not years - without burning your runway hiring an engineering team you'll have to manage yourself.
End-to-end AI solution delivery
Crafted AI solutions, built to solve the problems behind the brief.
Customer-facing products, internal tools, and AI workflows - built end-to-end by builders who've shipped AI in production, in weeks, owned by you.
Trusted by
ModjoLuminaraSeelkShapr
Who this is for
AI solutions, shipped - for four kinds of teams.
AI solutions take different shapes depending on who you are. We work with four kinds of teams.
Scaleups launching new AI lines
You have a product and customers. You need a parallel track shipping a new AI solution without slowing your core team's roadmap.
Enterprises modernizing operations with AI
You have data, processes, and people. You need AI workflows or internal tools that give your team leverage and move real cost or revenue metrics.
PE-backed companies with mature stacks
You're integrating AI into legacy products. You need senior builders who can navigate the existing system and ship cleanly.
What we deliver
Three kinds of AI solutions we deliver.
Same delivery model, three different shapes.
Customer-facing AI products
- What it is: Net-new SaaS, or AI features inside an existing product, used by your end customers.
- Examples we've shipped: Vertical AI SaaS, in-product copilots, AI-native onboarding flows.
- Problems we solve: Activation, retention, revenue, ARR, time-to-market.
Internal AI products
- What it is: Software your team uses internally, built to a product standard - not scripts, not Notion docs.
- Examples we've shipped: Sales research agents, ops intelligence tools, internal analytics platforms.
- Problems we solve: Time saved, decisions accelerated, output per headcount.
AI workflows & automations
- What it is: AI embedded into existing business processes - often for non-digital-native companies with rich data and manual operations.
- Examples we've shipped: Document processing, claims triage, knowledge retrieval over corporate corpora.
- Problems we solve: Cost reduction, throughput, error rate, cycle time.
How we ship
Where to start, depending on where you are.
Not every team starts at the same place - and not every phase has the same goal. Some phases are about learning fast, de-risking the build before you commit. Some are about earning fast, shipping production AI that moves real metrics. Pick the entry point that matches yours.
| If you're here | Mode | Start with | What you'll walk away with |
|---|---|---|---|
| Still exploring - too many ideas, no clear path | Code to learn | AI Discovery Sprint (1-2 weeks) | A prioritized roadmap with scoped milestones, costed delivery, and a clear go/no-go on each idea. |
| One idea, not yet validated | Code to learn | AI Prototype (3-4 weeks) | A working prototype in your users' hands, with real feedback and a decision-grade build/kill recommendation. |
| Validated scope, ready to build | Code to earn | AI Solution Build (8+ weeks) | A production AI solution shipped to real users, code transferred to you, ready to evolve. |
| Existing product, need to add AI | Code to earn | AI Integration (scoped per case) | New AI capabilities shipped inside your existing stack, without disrupting your core team's roadmap. |
AI-native, concretely
We don't just say AI. We ship it.
Every engineer on the team works with modern AI tooling and ships AI-native features in production. Here's what that looks like in practice.
LLM features in production
Streaming responses, tool use, function calling.
Used in the Modjo AI layer for turning sales calls into structured insights. Jump to proof ->
RAG pipelines
Vector DBs, hybrid search, evals.
Used in an internal review workflow for knowledge retrieval across operational documents. Jump to proof ->
Agentic workflows
Multi-step agents, tool orchestration, eval harnesses.
Used to ship new AI-enabled product capability without slowing the core roadmap. Jump to proof ->
AI-assisted delivery
Our team ships with Cursor, Claude Code, and modern dev workflows.
Used across the Luminara build, from first wireframe to first paying customers. Jump to proof ->
How we work
How we work.
Most agencies say "crafted." Here's what that means at Ludotech, in concrete terms.
Lean delivery, managed by metrics
We work in continuous flow - Kanban with just-in-time prioritization - so the next thing built is always the next most valuable thing. You get two weekly readings: flow (cycle time, what's shipping) and quality (test coverage, defect rate). Decisions made against numbers, not opinions.
We scope the problem before the feature
No spec lands without a clear answer to what business outcome this moves. Discovery is the first phase, not an afterthought - and we'll tell you honestly when a feature shouldn't ship.
Code we're proud to hand over
We build software that lasts - documented, tested, maintainable. Built to a product standard rather than a project standard, designed to evolve, not delivered and forgotten. You can hire your own team to take it forward without rewriting it.
Proof
Solutions we've shipped. Problems we've solved.
Real founders. Real metrics. Real shipped products.
Customer-facing
"The team moved fast without treating the product like a prototype."
Founder Modjo
From prototype to production in 12 weeks.
Customer-facing
"They helped us turn a fuzzy product idea into something customers could actually buy."
Founder Luminara
From idea to first paying customers in 6 months.
Workflow
"Ludotech gave us senior delivery capacity exactly where the roadmap needed it."
Product leader Seelk
A new AI module shipped without slowing the core roadmap.
Internal
"The result felt like a product our team could keep using, not a one-off automation."
Operations lead Internal AI workflow
Manual review work reduced across a high-volume operation.
Pricing
How we price.
Honest, predictable, no surprises.
Discovery phase
Fixed scope, fixed price, fixed timeline. You leave with a roadmap and an envelope before committing to delivery.
Delivery flow
Monthly or phase-based, scaled to team size and timeline.
Typical engagement
A focused first version is scoped after discovery, with budget and timeline agreed before delivery starts.
Always included
Senior engineers only, code ownership transferred to you, continuous flow, weekly readings, demos as work ships.
FAQ
Common questions.
The questions founders ask us before signing.
How do you handle scope changes mid-build?
We make the tradeoff explicit: keep the date and change the scope, keep the scope and change the date, or increase the team. Nothing changes quietly.
Who owns the IP and the code?
You do, fully - transferred at every milestone, not just at the end.
What happens after launch - do you maintain it?
We can hand over to your team, stay on a support retainer, continue improving the product, or transition into embedded engineers under the Hire offer.
Can we transition the team into our org later?
Yes. The same Ludotech engineers can continue embedded in your team when that is the right path after launch.
What is the minimum engagement size?
Most projects start with a fixed discovery phase, then move into at least one focused delivery phase with a senior team.
Do you only work with SaaS or digital-native companies?
No. We build customer-facing products, internal products, and AI workflows for both digital-native and operations-heavy companies.
How is this different from a freelance team or a traditional agency?
Freelancers rarely own delivery end-to-end. Traditional agencies often separate strategy from implementation. Ludotech gives you a senior employed team that scopes, builds, ships, and hands over production software.
Do you sign NDAs, DPAs, or work in regulated industries?
Yes. We can work under NDA, align on data handling, and scope security or compliance requirements before delivery starts.
Have an AI solution to ship?
Let's scope it.
One call, one honest conversation. We'll tell you what we'd build, how long it'd take, and whether we're the right team for it.