Services

Six service lines. One engineering team.

From AI systems to the software and commerce infrastructure they sit on top of — scoped as a fixed audit, a pilot, or a full build, depending on where you're starting from.

AI / RAG

Retrieval-Augmented Generation

ACTIVE

Your answers already exist somewhere — in Confluence, Jira, support tickets, PDFs, and old Slack threads. We build the retrieval layer that finds the right passage and grounds an LLM's answer in it, instead of letting it guess.

What's included

Document ingestion & chunking, hybrid (keyword + vector) search, reranking, source-cited answers, and an evaluation harness so you can measure accuracy before launch.

Tech stack

Qdrant, LangGraph, Postgres, OpenSearch/Elasticsearch (where already in use), your choice of hosted or private model.

Best fit for

Teams with a large, messy internal knowledge base and a support, sales, or engineering team drowning in repeat questions.

AI / Inference

Private LLM Inference

ACTIVE

If your data can't leave your network, a public API is a non-starter. We stand up self-hosted inference — on your cloud or on-prem — so your team gets LLM capability without a third party ever seeing the input.

What's included

Model selection and sizing, GPU-efficient serving, autoscaling, semantic caching to cut inference cost, and monitoring for latency and drift.

Tech stack

vLLM, KAITO, Cluster API, HAMi, Valkey for caching, QLoRA fine-tuning where a base model needs domain adaptation.

Best fit for

Finance, healthcare, and any team under contractual or regulatory limits on where data can go.

AI / Harness

Agentic Systems & AI Harness

NEW

An agent that can take real actions needs real guardrails. We build multi-agent systems with explicit approval gates, audit trails, and defined boundaries — so automation speeds your team up without anyone losing control of what's happening.

What's included

Multi-agent orchestration, human-in-the-loop approval steps for high-stakes actions, tool/permission scoping, and full logging of what each agent did and why.

Tech stack

LangGraph, MCP (Model Context Protocol), A2A, your existing task and ticketing systems (Jira, Zephyr, Xray, etc.).

Best fit for

QA/testing lifecycles, internal ops workflows, and any repetitive multi-step process currently run by hand.

AI / Native Apps

AI-Native Application Development

NEW

Bolting a chatbot onto an existing product rarely works well. We design new products with agentic reasoning, retrieval, and inference built into the architecture from the first sprint — not stapled on afterward.

What's included

Product architecture, data model design, agent/tool design, and a working end-to-end application — not just a prototype.

Tech stack

Next.js, React, Spring AI, Java/Spring Boot, Postgres, Prisma, AWS/Azure.

Best fit for

Founders and product teams building a new AI-first product, not retrofitting an old one.

Platform

Website & SaaS Development

ACTIVE

Custom-built, not templated. A site or SaaS product that fits how your business actually runs today and can absorb new features as it grows, instead of forcing you back into a redesign every two years.

What's included

Fast, secure, mobile-ready builds; cloud-hosted SaaS with authentication, billing, and admin tooling included where needed.

Tech stack

MERN (MongoDB, Express, React, Node.js), Next.js, Tailwind CSS, Shadcn/ui, Java, Spring, PostgreSQL, Prisma, Cloudflare, AWS, Azure.

Best fit for

Businesses that have outgrown a page builder or need a product, not a brochure site.

Commerce

Ecommerce & ERP Integration

ACTIVE

A storefront that isn't connected to inventory and finance just creates more manual reconciliation. We wire commerce directly into your ERP so stock levels, orders, and books all stay in sync automatically.

What's included

Storefront build or migration, real-time inventory sync, automated order-to-invoice flow, and reporting that reflects what's actually happening.

Tech stack

Shopify, Medusa.js, ERPNext.

Best fit for

Growing ecommerce businesses still reconciling orders and stock by hand.

Trading systems

Algotrading Solutions

ACTIVE

Systems that execute your strategy exactly as specified, every time, with no emotional override at 2am. Built with the same production-grade rigor as our AI infrastructure work — monitored, backtested, and risk-gated.

What's included

Strategy implementation, backtesting against historical data, risk controls and position sizing, and live execution wiring.

Tech stack

PineScript (TradingView), Python, TimescaleDB, AlgoTest, and broker integrations including Zerodha (Kite), Dhan, and Interactive Brokers.

Best fit for

Traders with a defined, rules-based strategy who want it running systematically instead of manually.

F.A.Q.

Common questions

How is a project priced?

Most engagements start as a fixed-cost audit or pilot so both sides know the scope before committing to anything larger. Larger builds run on a milestone-based fixed cost or time-and-materials basis, agreed up front — never open-ended.

What does the AI Readiness Audit cost, and how long does it take?

It's a fixed-scope, fixed-price engagement, typically completed within two weeks. [Insert current price band here, e.g. "from €X"] — contact us with your use case for an exact quote.

Which time zones do you work in?

We work with clients across EU and US time zones and structure check-ins to overlap with your team's working hours.

Do you provide support after launch?

Yes. Every build can move into a maintenance or monitoring retainer once it's live, sized to the system's actual usage and risk profile.

Can you work with data that can't leave our infrastructure?

Yes — this is exactly what our private inference service line is built for. Models and pipelines run inside your own VPC or on-prem environment; nothing is sent to a third-party API unless you choose to.

Next step

Not sure which service line fits?

Tell us what's slow or manual today, and we'll recommend the smallest scoped step that proves the concept.

Book an appointment →