ABOUT STIV LABS
We help companies remove manual work, build working digital processes, and launch solutions - from classic automation and custom development to AI workflows.
We start with the process, the problem, and the result. In some cases you need AI. In others, a solid integration, a service, or reliable automation without extra complexity is the better answer.
WHAT WE WORK WITH
Clients come to us when the process depends on people instead of a properly built system.
People stay important in the process. The problem is that manual work does not scale well: as volume grows, delays, errors, and operating costs grow with it. We help teams turn their know-how into a strong working system so people spend less time on routine work and more time on decisions, control, and improvement.
Situation
When too many repetitive actions, manual checks, and unnecessary steps still sit inside everyday work.
What we do
We map the result the process should produce, break it into basic steps, and rebuild the flow so the system takes over routine work and AI is used where it adds value.
Client outcome
The team spends less time on repetitive work and more time on exceptions, quality control, decisions, and higher-value tasks.
Situation
When data must be copied by hand between email, CRM, spreadsheets, 1C, files, and chat tools; statuses drift, duplicates appear, and people move information manually between steps.
What we do
We define a single source of truth, remove duplicate paths, and connect the systems so they behave like one coherent operating chain.
Client outcome
The process becomes clearer and more reliable: fewer manual transfers, fewer errors, faster status updates, and a shared version of the data.
Situation
When the team spends most of its time on the same repetitive tasks and the focus shifts from delivery to handling a large amount of routine information.
What we do
We automate the routine. This is where AI often has the strongest effect - classification, extraction, first-pass handling, and prioritization.
Client outcome
The team spends less time on repetitive flows and more time on exceptions, control, and growth, so the company can scale without growing headcount at the same pace.
Situation
When there are several problem areas and it is not obvious which one will produce the biggest effect first.
What we do
We help decide where automation will create the greatest leverage, what should be done in the first stage, and what can wait. If needed, we define a clear MVP to validate impact quickly.
Client outcome
The project gets clear boundaries, a realistic first step, and a decision based on evidence instead of assumptions.
HOW WE THINK
Not every task needs AI. Not every automation needs a complex architecture. The goal is not to add a trendy tool, but to build a solution that actually works in the business environment: clear scope, a real owner, integration with the existing stack, and a sane launch model.
That is why some projects need AI, while others need well-built classic automation or custom development without extra complexity.
REAL CASES
Below are a few anonymized scenarios from real work: no client names, but with a concrete problem frame and measurable result.
HR
Challenge: Recruiters spend 60% of time on initial resume filtering.
Solution: AI-driven scoring with configurable criteria and human review gates.
Result: 3x faster screening, 40% reduction in manual review hours.
OPERATIONS
Challenge: Manual data extraction from contracts and invoices.
Solution: Structured extraction pipeline with validation and exception routing.
Result: 85% of documents processed automatically with 97% accuracy.
KNOWLEDGE
Challenge: Teams waste hours searching across disconnected systems.
Solution: RAG-based assistant with source attribution and access control.
Result: 50% reduction in time-to-answer for internal queries.
WORK FORMAT
01
Problem and context review
02
Choosing the approach: classic automation, development, or AI
03
Design and launch
04
Handoff, support, and evolution
The launch matters, but so does what happens next: who uses the solution, how it fits into the process, and how it can evolve without creating chaos.
INTERNAL PRACTICE
For code work we have already automated most of the cycle, including tests and high coverage. But code is only part of a working solution: depending on the project, implementation is about 30-50% of the total cycle, while the rest is architecture, context, integrations, business logic validation, and launch. We use the same model inside STIV Labs in our own operating loop.
Code generation can be almost fully automated. A real working solution cannot.
See also: transition to AI-native organization and how we use AI inside STIV Labs.
WHERE WE ARE ESPECIALLY USEFUL
NOT THE BEST FIT
FOUNDER
Ivan Starastin is a practitioner who helps turn chaos into working systems at the intersection of process, architecture, development, and AI. 20+ years in IT and digital products, with 10+ years in ML, neural networks, and AI. The background spans large programs, high-load enterprise systems, fintech, and startup environments. The strength is not just understanding technology, but shipping solutions that actually work for the business and produce results.
The STIV Labs focus is on building a working process, service, or system that creates real business value.
NEXT STEP
Some cases need AI. Others need integration, development, or straightforward automation. It is better to start with the task and the process, not with a guess about the technology.