Services / AI & Automation

AI that earns its keep.

Most AI projects never make it into production. Ours do. We build automation and AI systems that solve specific problems and pay back fast.

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Why us

Engineers, not consultants.

We ship working systems, not strategy decks. Every AI engagement results in deployed software.

Built on real LLMs.

Claude, GPT, open-source models. We pick what fits your problem, not what's hyped this quarter.

Production-ready from day one.

Monitoring, evaluation, fallbacks, error handling. Not a demo that breaks under real traffic.

Honest about limits.

We'll tell you when AI isn't the right tool. Most "AI use cases" don't actually need AI.

01 — What we build

Four common engagements.

Specific problems, specific systems. We don't sell "AI transformation."

Engagement 01

Customer-Facing AI Chatbots

Chatbots integrated with your knowledge base, your CRM, and your booking systems. Not generic ChatGPT wrappers — real assistants trained on your business that handle real work.

What we build
—Multilingual customer support chatbots
—Sales qualification assistants
—Appointment booking and triage
—Internal knowledge assistants for staff
—RAG systems with your documents
Typical timeline

4–12 weeks from kickoff to production.

Engagement 02

Internal Workflow Automation

Software that removes manual work from your team's day. Document routing. Email triage. Data extraction. Cross-tool automation that just runs.

What we build
—Email and message classification
—Document routing and approval workflows
—Data extraction from unstructured sources
—Cross-system synchronization
—Custom internal tools with AI assistance
Typical timeline

3–8 weeks for focused automations.

Engagement 03

Document Generation & Processing

Automated invoicing, contracts, reports, and PDFs at scale. We've built it for ourselves with Monte Giro — we can build it for you.

What we build
—Bulk PDF generation from templates
—Contract automation with AI review
—Invoice processing and matching
—Report generation from data sources
—OCR pipelines for scanned documents
Typical timeline

4–10 weeks depending on volume.

Engagement 04

Custom AI Integrations

LLM integrations into your existing software stack. APIs, agents, retrieval systems, evaluation pipelines. For teams that already have software but need to add AI capabilities.

What we build
—LLM integration into existing platforms
—Custom agents for specific workflows
—RAG and vector search systems
—Evaluation and monitoring pipelines
—Fine-tuning and prompt engineering
Typical timeline

6–16 weeks depending on integration complexity.

02 — Approach

Practical, not magical.

We don't sell "AI transformation." We sell specific systems that solve specific problems.

Step 01

Identify the leverage point.

Where in your workflow does AI actually help? Most "AI projects" fail because they target the wrong problem. We start by asking what would create real value if it worked — and what wouldn't.

Step 02

Prototype fast.

We build a working prototype in days, not months. You see real output before committing to scope. If the prototype doesn't deliver, we kill it cheap.

Step 03

Ship a real system.

Production-ready integration with your existing stack. Monitoring, evaluation, fallbacks — all included. Not a demo that breaks under real load.

Step 04

Iterate.

AI systems need ongoing tuning. Prompts drift, data changes, models evolve. We stay involved or hand off cleanly with documentation.

03 — Tools

What we build with.

We pick tools based on the problem, not the press release.

LLMs

Claude (Anthropic) — preferred for most production work · OpenAI GPT — when client requires it · Open-source models — Llama, Mistral, Qwen · Local deployment — for privacy-sensitive workloads

Frameworks

Custom-built — for production systems · Vercel AI SDK — for fast prototypes · No LangChain — too much abstraction overhead

Vector databases

Pinecone · Weaviate · pgvector · Qdrant · Custom solutions for small datasets

Automation & workflow

n8n — for visual workflow builds · Make — for simple integrations · Custom Node.js / Python — for complex systems

Evaluation & monitoring

Custom evaluation pipelines · LangSmith — when client requires it · Built-in monitoring and alerting

Deployment

Self-hosted on managed infrastructure · Vercel / Cloudflare for edge deployment · On-premise when data sensitivity requires it

We avoid stacks that lock you in. Everything we build should be portable, debuggable, and replaceable.

04 — Honesty

When AI isn't the answer.

Half the AI consultations we do end with us telling the client they don't need AI. That's by design — we'd rather lose the engagement than ship something that doesn't work.

Wrong fit 01

A database query would do it.

If the problem is solvable with SQL or a structured lookup, AI is the wrong tool — slower, more expensive, less predictable.

Wrong fit 02

The data is too thin.

Not enough volume to train on, not enough corpus to retrieve from. AI needs ground truth — and not all problems have it.

Wrong fit 03

Accuracy needs to be 100%.

If "mostly right" isn't acceptable — legal, medical, financial calculations — current models can't deliver. A deterministic system can.

Wrong fit 04

The cost of being wrong is high.

Automation that's right 95% of the time is great for some workflows and catastrophic for others. We'll tell you which one yours is.

Wrong fit 05

A deterministic algorithm beats it.

For many problems there's a known, cheaper, more reliable algorithm. We'll use it instead of pretending AI is a magic upgrade.

If it does fit

We'll quote you fairly.

If AI is right for your problem, we'll scope it honestly. If it's not, we'll point you toward the right approach — even if it's not us.

05 — Common questions

Things people ask.

Which LLMs do you prefer?

Claude for most production work — better at following instructions, less prone to hallucination, better at nuanced output. We use OpenAI when clients require it or for specific capabilities. Open-source models when data sensitivity requires local deployment.

Can you work with our existing AI tools?

Yes. We integrate with whatever you're using — OpenAI, Azure AI, Google Vertex, AWS Bedrock, self-hosted models. We don't push proprietary stacks.

Will our data be used to train models?

No. We only use APIs that don't train on customer data, or we deploy open-source models locally for sensitive workloads. We document data flow explicitly in every engagement.

How do you handle hallucinations?

With architecture, not hope. Retrieval grounding, output validation, structured outputs where possible, and human-in-the-loop for high-stakes decisions. We design for failure modes, not best cases.

Can you fine-tune models on our data?

Sometimes. Most problems we encounter are better solved with retrieval (RAG) than fine-tuning, which is more expensive and less flexible. We'll tell you honestly which approach fits your problem.

What's the ROI on AI projects?

Depends entirely on the problem. We've seen 10x returns within months on document automation, and zero returns on chatbots that didn't fit the use case. We help you scope to maximize the chance of real ROI.

Do you offer ongoing AI training for our team?

Yes, available as part of retainers or as standalone workshops. We focus on practical AI engineering, not prompt-engineering hype.

Can AI replace our [specific role]?

Almost never. AI augments roles, it rarely replaces them. If someone is selling you "headcount reduction via AI," they're probably overpromising.

What if the model gets dumber over time?

It's a real risk. We build evaluation pipelines into every system so you'll know if quality drops before your users do. We also document fallback strategies for model degradation.

Do you build AI agents?

Yes, but cautiously. Most "agent" implementations are over-engineered for problems that don't need agentic behavior. We use agentic patterns when they genuinely fit — not because they're trendy.

Have an AI use case worth building?

Most don't make it past the demo phase. Let's talk about whether yours should — and what the right approach actually is.

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