AI agents for your business
Our agency builds AI systems adapted to your exact business needs and use cases
Trusted By
Startups, Scale-ups, Governmental institutions and Big 4 consulting firms
Our services
Three ways to work with us: AI audit and due diligence, custom AI solutions in production, and hands-on AI training for your team.
AI Audit & Due Diligence
We assess your systems, data, and team to find where AI pays off and where it does not. Useful before you build, and for investors before they invest.
Use case
Technical due diligence for a private equity firm: code, architecture, scalability, and the real risks, summarized for an investment decision.
Custom AI solutions
We build AI systems for your exact workflows: workflow automation, multi-agent systems, and assistants that answer from your own data.
Use case
Multi-agent system where one agent retrieves data, another runs the calculations, and a third checks the result against your rules.
AI transformation for teams
We help your team put AI to work in daily tasks, with training and hands-on support so the habit sticks after we leave.
Use case
Workshops and a tailored playbook so a team can run, trust, and extend the tools we build together.
The process
A straightforward process focused on delivering results
1. Discovery
Your workflows and technical infrastructure analyzed to identify where AI delivers ROI.
2. Building phase
Get a working prototype with your real data, then move to production.
3. Delivery & production support
Your system shipped to production with ongoing support and maintenance when needed.
Success stories
Blackfin
A private equity firm needed to judge a fintech’s technology, architecture, and engineering team inside a short diligence window. We ran a full technical due diligence covering the data room, the architecture, scalability, code quality, and AI-readiness, with deep-dive sessions alongside the CTO. The firm got a clear, evidence-based read on the technical moat, the real risks, and where AI could extend the product, in time for the decision.
Pigment
Enterprise teams were running complex, multi-step data workflows by hand. We built a multi-agent system where specialized agents coordinate retrieval, calculation, and validation across each step, turning that work into one reliable, automated workflow at scale.
Airsaas
Turning free-form input into structured project data was slow and manual. We built a multi-agent architecture where planning and execution agents work together to convert natural language into structured, validated outputs, so complex generation workflows run end to end with little manual work.
Weglot
Automated translation had to stay accurate on technical terms without hurting search. We built a RAG system with custom embeddings and several verification layers, so translations hold their domain accuracy while meeting SEO requirements at scale.
Isai
Sales data entry into the CRM was manual and easy to skip. We built a natural-language interface connected to the CRM through Telegram, so the team updates records from where they already work and structured data lands automatically, with no new tool to learn.
MerciApp
Generic grammar checking couldn’t cover the product’s needs. We built custom NLP models and integrated them into the live product, delivering real-time, context-aware writing suggestions that go well beyond basic grammar.
Ready to be the next success story?
Use cases
Where AI is already paying off, grouped by the part of the business it touches.
Commerce and customer support
Qualify and prioritize the sales pipeline
Reps were spending hours qualifying inbound leads by hand. AI now enriches each CRM record, scores buying intent, and pre-qualifies every lead before a rep opens it, so the team works the prospects most likely to close instead of sorting through the rest.
Answer quotes and tenders faster
Thousands of small quote requests went unanswered simply for lack of time. A multi-agent system now reads each request, prices it from past deals, and drafts the quote. Routine ones go out automatically, and the larger ones reach a person with the numbers already in place.
Automate customer responses by text or voice
As support volume spiked, first-response times kept slipping. An assistant now answers from the knowledge base across email, chat, and voice, resolving routine requests on its own and handing the harder ones to an agent with the full context attached.
Operations: IT, legal, finance, HR
Accelerate software development
Engineering throughput was capped by manual coding, testing, and review. We help teams adopt AI coding assistants, with training tied to their real day-to-day work, so developers move faster across writing, debugging, and refactoring without piling up technical debt.
Automate invoice and purchase checks
A high volume of supplier invoices was being checked by an outside team. AI now reads each PDF, extracts the line items, and compares prices against the negotiated contracts, so overbilling that used to slip through gets flagged on its own, ready to recover.
Check regulatory compliance
Auditors were spending most of their time reading client documents by hand. AI now reads the documents, compares them against the reference framework, and flags the critical gaps. Analysts confirm the findings instead of combing through every page.
Marketing
Produce optimized content at scale
Marketing had to ship more content across more channels on the same budget. AI drafts text, images, and video from a brief while the team shifts to direction and editing, producing far more output per person while brand voice stays under human control.
Generate and translate product descriptions
Hundreds of product sheets had to be written, translated, and tuned for search. AI generates and translates them straight from the product data, with a human check before publishing, turning a job that took months into one that takes minutes.
Personalize product recommendations
A rich purchase history sat unused beyond basic reporting. A recommendation engine now learns from it and feeds the site, emails, and loyalty space with suggestions that stay relevant, growing basket size and cross-sell without manual merchandising.
Industrial operations
Optimize a production line
Yield losses were analyzed only after the fact, and many failures went unresolved. AI reads machine and quality data inside the operators’ own tools to flag drifts early and recommend settings, so teams spend far less time on failure analysis and waste less material.
Reduce scrap and improve quality
Manual visual inspection was slow and missed defects on shiny metal parts. Computer vision now inspects every part and sends only the doubtful ones to a person, catching defects earlier without slowing the line.
Assist the engineering office
Decades of project knowledge sat scattered across plans, drawings, and notes. A retrieval system over the engineering archive answers technical questions and supports pricing, so teams spec new projects faster and that expertise survives as people retire.
Cross-functional
Speed up everyday office work
Teams were losing hours to meeting notes, translations, and document summaries. Simple AI assistants handle that repetitive writing inside the existing office suite, giving people their time back for higher-value work and keeping documents more consistent.
Make internal data easy to find
People lost time hunting for information across internal documents. A retrieval system (RAG) indexes those sources and answers in plain language with cited, controlled responses, so anyone gets a reliable answer fast, and the documentation finally gets used.
Capture and pass on internal knowledge
Know-how lived in a few experts’ heads and walked out the door when they left. AI captures procedures and answers internal questions from a maintained knowledge base, so new hires get productive sooner and the company depends less on any single person.
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Build custom AI systems for your requirements
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Deployed for startups, scale-ups, national research centers and Big 4 consulting firms