The AI Timeline — Where You Need to Be
Today
Companies should be automating simple, repetitive workflows — document processing, data entry, email drafting, verification tasks.
In 2 Years
Departments of 20 will run with 5 — managing AI workflows and handling the work that requires human judgment. Strategic thinking, client relationships, complex decisions.
Waiting doesn’t save money. It costs money — you just don’t see the invoice yet.
What’s Worth Automating — And What’s Not
Every organization is different. But after deployments across public sector and mid-sized companies, we’ve learned which types of processes deliver ROI.
High ROI — Worth Prioritizing
| Process Type | Why It Works |
|---|---|
| Repetitive manual tasks | Same steps every time, even if complex — AI handles volume |
| Document & data processing | Contracts, forms, intake, extraction — high volume, rule-based |
| Email & communication automation | Drafts based on your tone, templates, and context — humans tweak, not write from scratch |
| System integration | Connecting tools that don’t talk to each other — eliminates manual handoffs |
| Verification & list checking | Employees scanning spreadsheets, verifying data, spotting errors — AI checks faster and doesn’t get tired |
Lower ROI — Often Overhyped:
- “AI exploration” without defined success metrics
- Replacing judgment-heavy decisions too early
We assess your specific situation and help you pick the right battles first.
We Build Both On-Premise and Cloud AI
On-Premise AI Infrastructure
For organizations with the highest privacy and security standards. We physically build AI servers, configure local model deployment, and ensure your data never leaves your premises.
Best for: Government, healthcare, legal, financial services, sensitive IP
Cloud AI (+ Data Privacy Guarantees)
For fast rollout and access to the latest models. We deploy on Azure (EU data residency), with GDPR compliance and European AI Act alignment built in.
Best for: Speed to production, scaling quickly, latest model capabilities
Hybrid
Many clients use both: sensitive data stays local, general workloads go to cloud. We architect for flexibility.
We Build AI That Evolves With the Technology
The AI landscape changes monthly. New models, new tools, new capabilities. Most AI implementations become outdated the moment they’re finished.
Our approach: Architecture over tools.
| Layer | What Changes | How We Handle It |
|---|---|---|
| Models | GPT-5, Claude 4, Llama 5, better local models | Abstraction layers — swap models without rebuilding |
| Tools | Orchestration and automation tooling evolves fast | Modular architecture — tools are replaceable, not hardcoded |
| Agents | From task automation to full department agents | Systems designed to scale from simple workflows to autonomous agents |
In practice: New model drops? We swap it in — your system gets smarter automatically. Better orchestration tool emerges? We migrate without disrupting operations. AI agents become production-ready? Your infrastructure is ready to deploy them.
Case Studies
Case Study — Government Client (30,000+ Users)
Client: Government Agency
Challenge: Roll out AI capabilities to 30,000+ users while meeting strict security, privacy, and compliance requirements.
What we delivered:
| Project | Result |
|---|---|
| IT Asset Management system | Central registry of all software, hardware, assets & dependencies — delivered in 4 months, previously done in 2 years |
| Domain knowledge chatbot | Scalable RAG system deployed to 30,000 users |
| Data cleaning | 5 million lines cleaned with AI — planned for 5 people over weeks, done in days |
| Infrastructure | On-premise AI servers, compliant with government standards, GDPR, EU AI Act |
Key insight: Government-grade security and startup-speed delivery aren’t mutually exclusive — if you know the stack.
Case Study — Logistics Company
Client: Mid-sized logistics company
Challenge: Manual processes across planning, sales, and HR were limiting growth and burning out the team.
What we delivered:
| Project | Result |
|---|---|
| Route pricing requests | AI agent handles pricing automatically, employee checks in 1–5 minutes |
| Route, employee & truck planning | 5 FTE — AI handles base plans and starting tasks, planners focus on complex work |
| Invoice & list verification | AI scanner checks automatically, flags exceptions only |
Key insight: 4 FTE equivalent in time saved. Team focuses on client relationships and complex planning, not repetitive checks.
From First Call to Production in 8–14 Weeks
| Phase | What Happens | Duration |
|---|---|---|
| Assess | Identify highest-ROI use-cases, technical feasibility | 1–2 weeks |
| Architect | Infrastructure design, security, compliance plan | 1–2 weeks |
| Build | Working system, integrated, tested | 4–8 weeks |
| Deploy | Production rollout, monitoring, knowledge transfer | 1–2 weeks |
No endless discovery. We build.
