Custom-built
products with LLMs at the core —
for problems where the cost of error is high

We turn expensive expert routine into an LLM product. With precise quality metrics, validation on real data, and the security perimeter you need.

[ Examples ]
  • Financial audit of companies based on internal documents
  • On-device assistants for aviation
  • Automated contract review
  • B2B lead generation via tender parsing
  • Banking reporting automation
[ What you get ]
  • PoC in days–weeks — feasibility verified before major investments
  • 2–4× faster than typical teams
  • Code + process + evals — no black box
  • Production-grade in regulated domains
Discuss your project →

What we do

Core offering — turnkey LLM products. Separately — process delivery and training

01

Turnkey LLM products

From problem to runnable system. Done in fintech, aviation, legal, infrastructure, healthcare. Three types of work we specialize in:

Build · A

B2B sales automation

Lead parsing, catalog matching, scoring, auto-generated proposals, delivery into CRM or Telegram for the sales team

Build · B

RAG / Agentic Search

Financial statements, tenders, aviation manuals, legal contracts, medical records. Traceability — every answer carries a quote, a page, and an explanation

Build · C

On-device and edge

Local inference and retrieval directly on the device: mobile hardware, remote sites, regulated perimeters. Works offline. Our own inference optimization stack

+ separate services
02

Engineering process for AI teams

We hand over a reproducible process to your team: evals, monitoring, decision log, review gates. When useful — help hiring in-house AI engineers

03

Training

Two B2B programs (for dev teams and for non-developers) and a B2C course aiforwork.courses

Cases

Primary cases — FinTech / LegalTech, B2B sales, HRTech

01
Case · Financial Valuations Tivaco · Singapore top-5% damages experts globally · $15B+ in valued assets
Financial valuations for arbitration
Metrics from financial statements — for pre-trial business audits

~10 documents in ~5 minutes instead of several days per document. Every number carries a quote, a page, and an explanation

high-stakes valuations traceability
Read more→
02
Case · Aviation On-Device Overwatch AI · US Techstars · Startup of the Year — Aviation Festival Asia
Startup of the Year at Aviation Festival Asia
On-device RAG for pilots + agentic system for ground operations

Techstars · Startup of the Year at Aviation Festival Asia · pilot project with the largest European airline · two in-house AI engineers hired

on-device offline RAG aviation
Read more→
03
Case · Tenders / Pipes NDA · RU
Tender registry with transparent scoring
Lead generation through tender parsing — with transparent scoring

A narrow stream of relevant leads instead of «the whole registry». We enter projects even before the tender is filed

B2B lead-gen tender parsing scoring
Read more→
04
Case · Tenders / Pumps NDA · RU
Pump performance curve
Tender parsing → pump matching by performance curve → ready proposal in Telegram

Matching against real performance curves, not a lookup table. Days → hours to respond

B2B automation catalog matching Telegram bot
Read more→
FEATURED · NDA ● FULLY LOCAL ON-PREM PERIMETER contract document approval correspondence systems of record unified contract context comparator LLM rules catalog remark cards → lawyer UI LLM
05
Case · Contract Review NDA · fully local perimeter
Automated contract review — so the lawyer sees right away what to look at

Hours → minutes on standard contract classes. The rules catalog is now an auditable artifact. Under the hood — our own two-phase engineering pipeline

on-prem local LLM contract review agentic pipeline
Read more→

Other work

How we work

Every stage is an exit point with a ready artifact in hand. You can stop, re-plan, or continue.

  1. Scope
    ~days

    Business problem and risk boundaries

    Goal, stakeholders, constraints, cost of error. Domain map and flow — in your language, not engineering jargon.

  2. PoC
    days–weeks

    Feasibility check

    A minimal core on the riskiest piece. If the task isn't solvable in the current configuration — we surface it before any large investment.

  3. Criteria
    ~week

    Acceptance metrics + decision log

    After PoC we lock acceptance criteria and the decision log — based on what's actually reachable.

  4. MVP
    weeks

    Working system for the pilot

    Minimal integration set. Evals for business stakeholders — quality in numbers that make sense to you, not just to us.

  5. Prod
    months

    Full scaffolding + handoff

    Interfaces, monitoring, evals as a tool for ongoing development. Handed over to your team.

[ Handoff ]

What stays with you

Full package — code, process, tools to keep developing the system.

Code
In your repository
Decision log
Why each thing was done that way
Eval suite
Ship new versions without fear
Monitoring
System quality in production
Process docs
Analysis + engineering pipeline
Onboarding
Sessions for your team
Support
Optional — monthly retainer for iterations, upgrades, and architecture calls
Hiring
Optional — help with in-house AI engineers
You get the product together with a process your team can keep developing on its own.

Who's behind it

RoleFounder · Custom AI Engineering
Pastex-AI Architect · CodeSignal (US HRTech)
Tenure8 years in industry · 4+ AI-native
GeoYerevan · works across all jurisdictions
Nikolay Sheyko
[ Founder · Grably Tech ]
Nikolay Sheyko
I run all communication with the client from requirements to acceptance. I build the AI architecture myself and own technical execution. For specialized tasks, I bring in experts from my network
Also:
  • a.Co-founder of entropy.talk — the largest Russian-language online AI conferences (up to 5K attendees)
  • b.Author of the Telegram channel @ai_grably — real engineering of AI products, no hype (Russian)
  • c.Author of aiforwork.courses — for non-developers who want to delegate routine work to agents
  • d.Lecturer in Computer Science at a university in Yerevan
[ Featured in ]

FAQ

Things we usually get asked on the first call

[ 09 Q / A ]
Q01We're in a regulated domain — do you work with that?+
Yes. Two perimeters: SOC2 cloud (Vertex AI / Azure) — with data residency, access policies, and audit — and a fully local one (on-prem, local models, no external APIs). Case 05 runs entirely on the local perimeter — not a single line of contract leaves the building.
Q02We already have an AI team. How are you different?+
We build the product together with the process for evolving it: evals tied to business metrics, monitoring, decision log. We stay current with the industry through the community around entropy.talk. In our cases, client systems often get simplified by ~10× without losing accuracy. If your team already does this — you don't need us. If you want them to — we'll help hire and set up the process.
Q03What does «no black box» mean?+
Code in your repository, decision log (why each thing was done that way), eval suite (so you can ship new versions without fear), monitoring, process documentation (analysis + engineering pipeline), onboarding sessions. Hiring help on request. Goal: by ~3 months after release you don't need us for daily work — only for the next big iteration.
Q04Can you do on-prem or on-device?+
Yes, both. We do on-prem ourselves (case 05); for the Russian market we have a vetted partner when their hardware and compliance expertise is needed. On-device — case 02 (aviation): RAG assistant on iPad without internet.
Q05What about data — PII, NDA, regulators?+
NDA gets signed before any details. After that, three modes: sensitive data → local perimeter (on-prem, local models, no external APIs); foundation-model power required → SOC2 cloud (Vertex AI / Azure) without training on your data; public data → no restrictions. The mode is decided at PoC, against your compliance perimeter.
Q06How long does it take?+

We almost always split a project into stages — this reduces risk and cost on the early side.

PoC — days to weeks. The first stage is a feasibility check at minimal investment. We strip out production scaffolding, build only the AI core, and verify the required accuracy is reachable. If it isn't — that gets fixed before any large spend.

MVP — weeks. Once feasibility is confirmed, we assemble a working system with a minimal integration set for pilot use.

Production — months. Full scaffolding: interfaces, integrations, monitoring, evals for ongoing development.

Side note: we've been working with AI-assisted development tools for ~4 years and deliver comparable quality 2–4× faster than teams just starting with them.

Q07Where are you and which jurisdictions do you work in?+
Legal entities in RU and Armenia. We work across all jurisdictions. Active clients — Singapore, US, EU, RU, Indonesia. The contract is structured around your perimeter.
Q08We have a prototype — can you finish it?+

Technically yes. But in most cases finishing the existing prototype is worse for you than rewriting it: longer timeline, higher cost, lower reliability — usually the prototype already has the wrong architectural choices baked in, and fixing them costs more than doing it right from scratch.

Standard play: the existing system serves as a requirements reference and as a test-data generator for the new one — meaning we don't run on data from nowhere, we test the new system against real inputs and outputs from the old one. Integrations and data are reused; the core is rebuilt.

Q09What does «hiring help» mean? Are you recruiters?+
Not recruiters in the classical sense. We help where the technical hiring of AI engineers usually breaks: designing the role itself (AI engineer vs AI-native developer — different people, different processes), designing the technical interview (CodeSignal experience), running interviews ourselves or training your developers to run them. Plus a network in the Russian-language AI community.

If you have a problem
you're scared to hand off to AI —
we want to hear about it.

Send your case. We get the context, scope the boundaries, and agree on a PoC. PoC usually takes days to weeks — that lowers your risk on the way in.

Telegram → t.me/nikolay_sheyko ↗ Email → nikolay.sheyko@grably.tech ↗