Most AI projects fail on the data underneath, not on the model. We build both layers — collection and pipelines that hold at 99.9% uptime, and the agents that run on top. Small senior pods, Estonian company, engineering team in Ukraine.
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Two things we go deep on, and the systems that sit around them. Deepest in travel and healthcare; the same problems show up in commerce and fintech, so we work there too.
Collection at volume from sources you don't own and that actively push back. Anti-bot handling and proxy rotation aren't add-ons here — they're the reason the uptime number holds. Delivered under SLA, normalised into one schema.
Natural-language access to your databases, retrieval over internal documents, workflows that used to need a person. Every answer traceable to a source. We care whether the agent is correct, not whether it's impressive.
Clinical systems that survive contact with a real clinic. Lab workflows, referral tracking, role-based access and audit trails. GDPR as a design constraint on the data model, not a checklist at the end.
Multi-tenant products built around how a business actually works. Roles that match the real organisation rather than a generic template. Live for 30,000 users in five months on the last one.
The work between systems that were each chosen for good reasons and none of which were chosen together. Error handling, retries and alerting, because automation that fails silently is worse than doing it by hand.
Anyone can write a scraper. Keeping it alive while the target changes its defences every quarter is a different job — and it's the one that decides whether the data still arrives in month twenty-four.
We're a member of the Claude Partner Network and build with Anthropic's Claude alongside the wider LLM ecosystem — shipping AI features real users rely on, not proofs of concept that stall.
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Client names withheld under NDA. Every figure below is from a production system.
600K origin-destination-time records a month across ten independent sources, normalised into one schema. Validation at ingest, so anomalies never reach the product. 99.9% uptime — under 45 minutes of downtime a month.
Activities, availability, pricing and reviews collected from platforms carrying five million monthly visits — and defended accordingly. No official API covers the full catalogue, which is why the work existed.
Sellers ask in plain language what customers complain about and how they compare. Every answer cites the records behind it. They rebuilt their offerings around it and grew revenue.
Referral to signed report across four research lines — histology, IHC, CISH, express. Four tracked workflow stages, four roles with separate rights, and an audit trail that makes every action attributable.
Live in five months from kickoff, with eight external integrations. The first two weeks produced no code and removed about 40% of the original scope — most of it workarounds for problems we could just remove.
A retrieval agent over a company's own contracts, specs and support history. Permission-aware, every answer cited. Roughly 60,000 questions a month now get answered without reaching a person.
Small on purpose. We work in two areas and turn down the rest.
No bench, no juniors billed as seniors, nobody added to a project to fill a line on an invoice. A pod is three to six engineers who have shipped this kind of system before.
You talk to the people designing and building the system. Nobody relays your question to someone you never meet and comes back with an answer two days later.
You contract inside the EU under EU law, with EU data residency where it matters. The engineering team is in Ukraine, distributed, with mirrored repositories and a documented continuity plan.
Half of what a client asks for is a workaround for something that could just be removed. Finding that out takes one conversation and saves months. We would rather tell you a project isn't a fit than sell you six months of the wrong thing.
Expertise first, team second. You find out whether we're useful before committing to anything long.
Thirty minutes. We tell you if it isn't a fit.
Two to four weeks, fixed price, real deliverable.
One working thing in production, not a slide deck.
If it worked, a dedicated team on a monthly basis.
SLA, on-call, and the boring work that keeps it alive.
Chosen because they hold up in production, not because they look current.
Partners and platforms we build on and alongside.
“Integrality delivered a solid, well-engineered data-collection solution for us.”
“A dependable engineering partner on our web-data work — clear communication and quality output.”
“Integrality built reliable parsers for our large-scale e-commerce data collection — strong engineering and easy to work with.”
“They delivered server-side rendering across thousands of product pages and clean GraphQL auth — senior-level work, on time.”
“Integrality helped us ship AI features in Python for MeetingMap — pragmatic, fast, and a real understanding of the product.”
We hire senior engineers for our client teams. One role is open right now.
The client is a Canadian SaaS company focused on landing-page optimization and CRM solutions, moving toward an AI-first product and engineering model — investing in data platforms, ML infrastructure, and agentic AI. We're looking for a Senior Data Engineer to support data engineering, warehousing, and ML enablement across data modeling, ETL, cloud infrastructure, and scalable data solutions.
Design and implement data-platform solutions, including:
Use the map above, or tell us directly — we'll turn the problem into reliable software.
