AI automation consulting
Start here when a manual process is eating senior hours, a no-code chain has hit its ceiling, or an AI idea needs a build plan before it gets budget.
Open serviceAI consulting · Vilnius & remote
AI consulting for production automation, LLM systems and agent workflows
Consulting for teams whose AI has to survive contact with production: automation that replaces manual work, LLM systems past the prototype stage, agents with real responsibilities.
Most AI projects don't fail at the model. They fail around it — no retries, no evals, no cost ceiling, no answer for the day a provider goes down. I work on that part: finding where AI can safely take over manual work, then building the smallest system that proves it in production.
The work is concrete: workflow audits, production hardening for LLM prototypes, agent operating models, RAG fit checks, internal tools, and rescue for AI systems that are already creaking.
Services
Five ways in, depending on where your system hurts.
LLM productionization
Start here when the prototype is convincing but nobody can say what it costs at scale, why it fails, or whether yesterday's prompt change made it worse.
Open serviceAI agent workflow design
Start here when the team wants agents, but nobody has decided what happens when one fails at 2am.
Open serviceRAG systems and knowledge workflows
Start here when knowledge lives in documents, tickets or transcripts, and answers need grounding, citations and boundaries.
Open serviceInternal tools automation
Start here when operators need a real tool — with permissions, audit trails and half-done states — not another spreadsheet and a pile of scripts.
Open serviceWork by area
The case studies, grouped by the kind of system you're planning — read the nearest one before we talk.
AI automation
For teams replacing manual or no-code workflows with automation that has to survive retries, duplicates and partial failure.
Open topicLLM systems
For teams past the prompt stage, now facing service boundaries, fallback paths, observability, cost control and evaluation.
Open topicAgentic workflows
For teams that need agents operating inside a controlled workflow, not improvising in a chat window.
Open topicProduction reliability
For teams whose AI or platform systems need deployment safety, data integrity under retries, and structural isolation between tenants.
Open topicHow an engagement runs
It stays small until the system earns more.
- 01
Audit the workflow: the model boundary, the data flow, the latency and cost budget, and the thing that actually breaks.
- 02
Pick a first slice that is useful on its own and honest about its risks.
- 03
Build or stabilize the production path — queues, retries, observability, cost controls, evals, deploys.
- 04
Hand off with an operating model: what to watch, what decides next, and how each part fails.
Guides
The decisions that come up before any of this is worth building — automation vs no-code, agents vs workflows, whether RAG earns its keep.
AI automation vs no-code automation
When no-code is enough, when it quietly becomes a liability, and how to move to real automation without throwing away what already works.
Read guideAI agents vs AI workflows
Most systems sold as agents should be workflows. How to tell which one you need, and what an agent must have before it touches production.
Read guideRAG fit check
Retrieval-augmented generation solves a specific problem. Three checks that tell you whether you have that problem — before you build the pipeline.
Read guideCommon AI project failure modes
AI projects rarely die at the demo. They die months later, in production, for reasons that were visible on day one.
Read guideDeployment safety for AI systems
Why health-gated rollback and migration discipline matter for an AI system as much as latency or cost.
Read guideQuestions worth asking first
- When is AI automation worth building instead of using no-code tools?
Build when failure has a price: when the workflow needs reliable state, retries, permissions, an audit trail or custom model behavior. Stay on no-code while the process is low-risk, reversible and still fits the tool — it's the cheapest way to prove demand.
- Do I need an AI agent, or just a workflow with LLM steps?
If you can write the steps down, it's a workflow — build that, it's cheaper to run and easier to debug. An agent earns its place only when the system genuinely has to choose between tools mid-task, and only once state, approvals and recovery are designed.
- When is RAG the right answer?
When answers must be grounded in knowledge that keeps changing — policies, documentation, tickets. But RAG alone decides nothing: corpus quality, measured retrieval, permissions and a designed "I don't know" are what make it trustworthy.
- What usually breaks when an LLM prototype reaches production?
Rarely the model call. What breaks is everything around it: latency under real load, cost at real volume, retries, provider outages, prompt drift nobody noticed, and failures nobody can explain because observability was left for later.
- How should a first AI consulting engagement be scoped?
One workflow, one clear decision, one production slice. The first engagement should reduce risk before it grows scope — if the first proposal is a six-month build, someone skipped the diagnosis.
Bring the messy workflow
A good first call ends with a sharper diagnosis, a safer architecture direction, and a next step small enough to actually happen.