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Applied AI & Automation

AI that survives an evaluation, not just a demo.

Getting an impressive AI demo takes an afternoon. Getting a system that behaves correctly on the thousandth request, in front of a customer, at a cost you can defend, is an engineering discipline.

We start from the workflow, not the model. Which decision are we automating, what does 'wrong' cost, who reviews it, and how will we know if quality drifts next quarter? Then we build the smallest system that answers those questions and measure it continuously.

Every AI engagement ships with an evaluation suite, a guardrail layer, a human-review path for consequential actions, and a cost dashboard per feature. If we cannot measure it, we do not ship it.

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Capabilities

What this practice actually covers.

01

Assistants & agentic workflows

Task-focused assistants that use your tools and your data, with a plan a human can inspect and a stop button that works.

  • Tool-using agents with typed schemas and permission scoping
  • Deterministic orchestration for multi-step business processes
  • Human-in-the-loop approval gates on consequential actions
  • Full trace logging and replay of every run
02

Retrieval & knowledge systems

RAG done properly: the retrieval layer is where quality is won, and most of the work is in chunking, ranking and permissioning rather than the prompt.

  • Document pipelines with structure-aware chunking and metadata
  • Hybrid search, re-ranking and citation-backed answers
  • Row-level permissions so answers respect your access model
  • Freshness and index-drift monitoring
03

Evaluation & quality engineering

The evaluation suite is the product. It is what lets you change a prompt or upgrade a model on a Tuesday without a rollback on Wednesday.

  • Golden datasets built from your real traffic and edge cases
  • Automated scoring, LLM-as-judge with human calibration
  • Regression gates in CI on every prompt and model change
  • Live quality monitoring with drift and outlier alerting
04

Safety, guardrails & governance

Input and output controls, injection resistance and an audit trail — plus the documentation your compliance team will ask for under the EU AI Act.

  • Prompt-injection and data-exfiltration testing
  • PII detection, redaction and data-residency controls
  • Output validation, refusal handling and fallback behaviour
  • Model cards, risk assessment and AI Act documentation
05

Process automation

Most of the value is not a chatbot. It is the document that no longer needs a human to read it, the ticket that routes itself, the report that writes its own first draft.

  • Document extraction, classification and validation pipelines
  • Intelligent routing, triage and enrichment for support and sales
  • Back-office automation with exception queues for humans
  • Integration into existing ERP, CRM and ticketing systems
06

Cost, latency & model operations

Model choice is an engineering trade-off with a monthly invoice attached. We tune it deliberately and keep it visible.

  • Model routing — small models for the easy 80% of traffic
  • Prompt caching, batching and streaming for latency and spend
  • Per-feature cost dashboards with budget alerts
  • Fine-tuning or distillation when it beats prompting on cost

Deliverables

What lands in your account.

  • Production AI service with versioned prompts and configuration
  • Evaluation suite, golden datasets and CI quality gates
  • Guardrail layer, red-team report and incident playbook
  • Cost, latency and quality dashboards
  • Model card and EU AI Act documentation pack

Stack

  • Claude
  • OpenAI
  • Llama
  • LangGraph
  • LlamaIndex
  • pgvector
  • Python
  • TypeScript
  • Vercel AI SDK
  • Braintrust
  • Temporal

Chosen per engagement, not by habit. If your team already runs something else and it works, we work in it rather than around it.

Staffed by

Every engagement in this practice is delivered by a named team you interview yourself — designed, hired and led through our team-building practice.

See the people

Engagement

Three ways to start.

Indicative starting figures for a typical engagement, excluding VAT. Your actual number comes from a written scope — but it will be in the same neighbourhood.

AI Feasibility Sprint

Proving value and cost before you commit a roadmap.

Duration
2 weeks
Team
AI engineer + product lead
From
€12,500

AI Feature Build

Taking one high-value workflow to production.

Duration
6–10 weeks
Team
2–4 specialists
From
€62,000

AI Platform Retainer

Running and improving AI features already in production.

Duration
Rolling
Team
Dedicated squad
From
€18,000 / month

FAQ

Questions about this practice.

Ask us directly
Which models do you use?
Whichever passes the evaluation at the lowest cost — usually Claude for reasoning-heavy work, a small fast model for classification and routing, and open models where data residency requires it. We keep the provider swappable.
Will our data be used for training?
No. We use enterprise API tiers with training explicitly disabled, and we can deploy inside your cloud or on open models when the data cannot leave your perimeter.
How do you prove it actually works?
With a golden dataset drawn from your real traffic and a scored evaluation you can run yourself. You see the numbers before launch and the regression report after every change.

Often combined with

Get started

Tell us what you need built — or who you need building it.

Send us the situation as it actually is. You get back a shaped engagement, a named team, a fixed timeline and a number you can put in front of a board.

Start a conversation
besarta@blocify.io+49 1575 5863757

A partner replies within one business day — with questions, not a sales sequence.