Manual reporting, fragmented systems, person-dependent processes, and an inference-economics curve that rewards whoever moves first.
End-to-end: software architecture, AI integration, team enablement. One partner, one accountable plan.
Numbers shown here are placeholder ranges — they get sized to your actual baseline in the discovery sprint.
SLAs and milestones in writing from week one — not assembled at the end.
Frontier models (Claude, GPT, Gemini) are now production-stable. Reliability stopped being the blocker; integration design did.
Token costs are down ~10× in 18 months. Use cases that didn't pencil out a year ago now have positive unit economics.
Your peers are already shipping internal copilots, automated reporting, AI-led customer service. The gap widens monthly.
EU AI Act, NIS2, and sector rules turn governance from a nice-to-have into a date on the calendar. Latecomers retrofit.
Instant answers, smart defaults, contextual help. The bar got reset by ChatGPT, and B2B buyers now bring that bar to your product.
Senior engineers and AI specialists are competed for globally. Modernization needs to come from leverage, not headcount.
The fastest movers are 12–24 months in. Their data flywheels are already turning. Catching up later costs disproportionately more.
EU AI Act takes effect in phases. Sector-specific rules tighten alongside it. Compliance built in is cheaper than compliance bolted on.
Software ages a company. Software makes it young again.
We measure every software system on the same seven pillars. Each one is a concrete engineering surface — and together they determine whether your foundation can carry the next decade of AI workloads.
Every slide that follows shows one of these in action.
Talent leaves for AI-native peers. Customers churn quietly to faster competitors. Inference contracts get more expensive as you arrive late. EU AI Act compliance turns into emergency work. Retrofit costs ~3× building it in.
Lock in lower inference costs. Retain talent through interesting work. Build the data flywheel that compounds for the next decade. Ship under the regulatory window. Make modernization a moat, not a cost line.
System design, integrations, event-driven microservices, MLOps. The plumbing AI sits on top of, designed to scale and to hand over.
RAG, agents, copilots, classifiers, automation. Not "chatbots glued on" — AI placed where it changes the unit economics.
Training, process redesign, documentation, adoption matrix. The system only works when your team owns it after we leave.
Headline ranges shown here are placeholders, scoped to a typical mid-market modernization. Real numbers get sized to your baseline in the discovery sprint.
Tied to budget lines, not vibes. Every number maps to a workstream.
POs → invoices → bank statements matched automatically. Exceptions go to a review queue, not a spreadsheet.
Forecasts trained on your order history + supplier lead times. You get a stockout alert three weeks before it happens, not the morning of.
Trained on your knowledge base + ticket history. Confident answers, clean handoff, full audit trail.
Trained on your SOPs, your policies, and recorded interviews with senior staff. New hires answer their own questions in week one.
Runs on cameras you already have. 200 ms per part. Closes the loop into yield analytics, not a separate dashboard.
Prioritized pipeline. AI-drafted proposals with your tone, your pricing, your win-rate signals.
Walk-through using Surge — a US solar-construction operator we rebuilt from spreadsheets to a full vendor-and-deal platform.
Vendor portal + ops console on a typed API, event-bus for project lifecycle, AI services for document QA and lead scoring, integration adapters for CRM/ERP/email, audit-grade logging end-to-end.
From US construction CRMs to a €2M German exit.
Solar contractor management with 250+ vendors
Built and scaled to a €2M exit
Diplomatic news hub for European policy readers
Day-one tech partner; now revenue-generating
MCP CLI that rewrites prompts in real time
Driving instructor platform with PMF
B2B CRM & operations
A US-based solar contractor needed to coordinate hundreds of installation vendors, jobs, and homeowner deals — all of it running on spreadsheets, email and gut feel.
A purpose-built CRM and operations platform: vendor onboarding, deal pipeline, project tracking and team management — designed for the field and built to scale alongside the business.
A €2M acquisition.
A founding team in Germany had a strong consumer concept around QR-coded memorial products — but no in-house product or engineering capacity to bring it to market and scale it.
Two years of full-stack product partnership: web platform, QR provisioning, customer flows and the supporting infrastructure to operate at retail scale across the German market.
An editorial team in the Netherlands needed a publishing platform serious enough for diplomatic and policy readers — fast, credible, and easy to maintain across a growing newsroom.
A custom news platform with a tailored CMS, structured content workflows for editors, performant reader experience, and the SEO foundation a serious EU news publication needs.
A US founder needed an engineering partner who could carry the product from a blank repo through real customers — without burning the founder's budget on rebuilds.
End-to-end product development from day one: architecture, frontend, backend, integrations and iterative releases tracking what real customers were paying for.
Developers and data scientists know what they want from an LLM, but the prompts they actually type are inconsistent — and quality varies wildly because of it.
An MCP-powered CLI that rewrites prompts on the fly into stronger, more technical formulations — fully documented, with tunable parameters so each engineer can tailor the behaviour to their workflow.
Driving school instructors and prospective students in Georgia were matching through phone calls, classifieds and word of mouth — with no digital booking, payments or trust signals.
A two-sided marketplace platform connecting licensed instructors with students: profiles, scheduling, payments, reviews and the digitised flows that replace the old offline process.
Curricula tuned per role: ops, finance, engineering, leadership. Hands-on, not slideware. Recorded for new joiners.
Workflows updated to use the new system, not bolted on top of it. We retire steps that AI now handles.
Single source of truth, searchable, kept fresh through tooling. Tribal knowledge becomes institutional knowledge.
Per-team rollout plan with usage KPIs, success owners, and explicit handoff criteria. Adoption tracked, not assumed.
Strong product idea, no engineering capacity, no path to retail scale.
Two-year full-stack partnership: platform, QR provisioning, customer flows, retail-grade infra.
€2M acquisition
~24 months
Hundreds of vendors and deals running on spreadsheets, email and gut feel.
Purpose-built CRM & operations platform: onboarding, deal pipeline, project & team management.
250+ vendors · 1 system of record
~9 months
Offline matching via phone calls, classifieds, word of mouth. No bookings, payments or trust.
Marketplace platform: instructor profiles, scheduling, payments, reviews — full digitised flow.
Revenue + PMF
~8 months
Diagnosis, scoped pilot, sized ROI baseline. Signed before build starts.
Smallest valuable slice, real users, instrumented. Pilot KPIs reported weekly.
Full system live for the agreed scope, with adoption matrix tracking per team.
Documentation complete, runbooks tested, your team operates the system day-to-day.
Measured on systems we operate. Every incident gets a written post-mortem within 48 hours — what broke, why, what we changed.
Sev-1 acknowledged within 4 business hours, mitigation path within 8. Always a named owner.
Scope, risks, KPI deltas, decisions needed. No surprises at month-end.
Every workstream tied to a baseline number. Wins, drag, and what changes next month.
Documented model choices, evals, and known failure modes. No black boxes in production paths that matter.
Bias testing on representative slices. Guardrails on outputs in regulated and customer-facing flows.
We don't impose ours. Risk policies, approval workflows, and documentation slot into the governance you already run.
Prompt, retrieval, output, decision — all logged, queryable, and replayable for audits and post-incident reviews.
Each AI system mapped to its EU AI Act risk tier; high-risk paths flagged before they reach production.
Article-11 style technical files maintained alongside the code, version-controlled like the code is.
Users informed when interacting with AI. Generated content labeled. Disclosure flows tested, not improvised.
Override paths, audit logs, escalation routes for any consequential decision. Designed in, not retrofitted.
Drift detection, performance regression alerts, complaint channels — feedback loops back into the model lifecycle.
Where applicable, the artefacts and process to support conformity assessment are produced as a by-product, not a project.
A few conversations to understand the project, the idea, the constraints. We then play it back in our own words — "here's what we heard" — so we know we got it right before we build anything.
A small, non-working visual mockup — shaped immediately. We iterate until the foundation is solid: typically a month or less, sometimes up to six weeks. Everything after this rests on it.
Development broken into milestones agreed during the foundation phase. Every milestone ships something you can see and review — no opaque months of "trust us, it's coming."
No build starts before the foundation is right. No milestone ships without something to see.
No hourly drift. No open-ended T&M. One price, agreed up front — then paid as the work lands.
We size the project together — its scope, complexity, and milestones. From that, we propose a single price. You sign off before anyone starts. No surprises mid-build.
Half the agreed price is paid before work begins. This funds team allocation, tooling, and the foundation phase — the part where everything else gets shaped.
The remaining 50% is split across the milestones agreed during foundation. You pay a percentage as each milestone ships — and you've already seen what you're paying for.
You always know what you owe. You only pay for what's already in your hands.
Default toolkit, picked per fit. We don't impose a stack — we adapt to where you already are, then justify any change.
TypeScript and Python by default. Go where concurrency or systems-level fit matters. Rust where it earns its keep. Node 24 LTS, Python 3.13.
Next.js (App Router) for web. FastAPI / NestJS for backends. AI SDK v6 for agent and chat surfaces. Tailwind v4 for design systems.
Vercel for web/edge. AWS, Azure, GCP for backends. Hetzner / OVH / IONOS for EU-sovereign workloads. On-prem and hybrid where required.
GitHub Actions / GitLab CI, preview environments per PR. OpenTelemetry, Grafana, Sentry, Vercel Analytics. Tracing, logs, metrics from day one.
EU-resident by default. Customer-owned IP. Audit-grade controls baked in, not bolted on.
EU/Germany-hosted by default. Frankfurt, Hamburg, or Berlin regions across hyperscalers. On-prem and air-gapped deployments where required. No US-only by default.
TLS 1.3 in transit. AES-256 at rest. Cloud KMS or HSM-backed key management. Secrets in vaulted stores (1Password, Vault, cloud-native). No plaintext in repos.
OAuth2 / OIDC / SAML for enterprise SSO. Role-based access controls. Audit logs on every consequential action. Short-lived tokens; principle of least privilege throughout.
Data minimisation, right-to-erasure, processing records, DPA on request. Customer owns code, models, fine-tunes. Sub-processors disclosed and limited by contract.
Models picked per task. Evals on every change. Drift caught before users do.
Frontier (Claude, GPT, Gemini) for reasoning-heavy paths. Open-source (Llama, Mistral, Qwen) for cost or sovereignty. Specialised models per fit — embeddings, ASR, vision, structured extraction.
Hybrid retrieval (vector + keyword + graph) with chunking strategies tuned per domain. Tool-using agents with bounded plans, retries, and human-in-the-loop on consequential actions.
Golden datasets per use case. Regression suite gates every model or prompt change. Input/output filters, safety policies, escalation paths, and full audit trails on AI decisions.
Versioned models. Drift and performance regression alerts. Scheduled re-training and re-evaluation. Cost and latency budgets per surface, with circuit breakers when limits are hit.