Everything you need to know
before building with AI.

We build three types of AI systems for startups and SMBs:

AI Agents — Autonomous systems that handle multi-step tasks end-to-end without human intervention. They read inputs (emails, documents, database records), reason about them, and take action — extracting data, routing tickets, triggering downstream processes, and logging results. No human in the loop required.

Custom AI Software — Full product builds where AI is a first-class feature from day one: SaaS platforms with AI-powered capabilities, internal copilots that answer questions based on your own data, operational dashboards with AI-generated insights and anomaly detection.

Backend & Integrations — The engineering layer that makes AI products reliable: APIs, data pipelines, vector databases, cloud infrastructure, and third-party integrations. Often built alongside the first two, or as a standalone engagement when you already have a UI.

Everything we ship runs in your infrastructure, handles errors gracefully, and scales as your usage grows. We do not deliver notebooks or demos.

An AI Agent is software that uses a language model (LLM) to reason through a task and take a sequence of actions — calling tools, querying databases, sending messages, making decisions — until the task is complete, without a human doing each step.

Where a traditional chatbot just responds to questions, an AI Agent acts. For example: an Agent can read an email, extract invoice line items, validate them against your ERP, flag discrepancies, post the accounting entry, and send a confirmation — entirely autonomously.

Your business likely needs an Agent if:
— A team member spends hours each week on the same type of repetitive task
— That task involves reading, classifying, extracting data from documents or emails
— Errors in that process cost money, delay customers, or require rework
— You want to scale the process without proportionally scaling headcount

AI Agents & Automation — Scoped around a specific business process. We take one painful manual workflow and automate it end-to-end with AI. Typical delivery: 2–4 weeks.

Custom AI Software — A fuller product build: SaaS tools, internal knowledge bases, AI-powered dashboards. These involve frontend, backend, and AI layers and take 6–12 weeks. The AI is the differentiator, not an afterthought.

Backend & Integrations — Pure engineering. Solid APIs, data pipelines, vector databases, and cloud infrastructure to power AI products reliably. Often paired with service 1 or 2, or standalone if you have a frontend and need the AI plumbing.

In practice, most engagements combine all three: we build the Agents, surface outputs through a dashboard, and wire everything to your existing systems.

Yes — this is the most common type of engagement. Most clients already have a product, internal tool, or process in place and want to augment it with AI, not rebuild from scratch.

We are comfortable in existing codebases across Python, TypeScript, .NET, and Node.js. In the scoping session we review your architecture, identify the right integration points, and add AI functionality — a copilot sidebar, an extraction pipeline, a classification layer — without disrupting what is already working.

What we need to proceed: API access or database access to your existing systems, a clear outcome to target, and a staging environment to develop and test against.

Yes, RAG is one of our most-used patterns. It allows an LLM to answer questions accurately based on your own documents, database, or knowledge base — without hallucinating facts or going out of date as your content changes.

A typical RAG system we build:
1. Ingests your content (PDFs, Word docs, Confluence pages, database records, emails)
2. Chunks and embeds it into a vector database (Pinecone, Supabase pgvector, Weaviate)
3. At query time, retrieves the most semantically relevant chunks
4. Passes them as context to the LLM for a grounded, cited answer

We add confidence scoring, source citation with links, and human-review escalation for low-confidence answers. This powers our document intelligence, internal copilot, and knowledge-search use cases.

1. Free consultation (60–90 min) — We understand the problem, the current process, the data you have, and what "done" looks like. We ask hard questions about edge cases and success metrics.

2. Spec & estimate (3–5 days) — We produce a concise technical specification: what the system does, how it integrates, what the boundaries are, and a fixed timeline with milestones and a fixed price.

3. Build (2–8 weeks depending on scope) — Short sprints with weekly demos. You see progress every week, give feedback, and we adjust before the next sprint. No big reveal at the end.

4. Ship — We deploy to your infrastructure (or help set up cloud infra), run load and edge-case tests, and hand over documentation.

5. Measure — We stay engaged for 2–4 weeks post-launch to monitor outputs, tune prompts, and address anything that only appears in production traffic.

— AI Agent for a single process (e.g., invoice extraction, ticket classification): 2–3 weeks
— AI Agent with integrations and a simple dashboard: 4–6 weeks
— Internal copilot over your existing data: 3–5 weeks
— Custom AI SaaS product (MVP): 8–12 weeks

These assume a well-scoped spec, reasonable access to your systems, and a responsive point of contact on your side. The main variables that delay AI projects are unclear requirements and slow feedback loops — we scope tightly upfront to prevent both.

We use 60–90 minutes to go deep on your problem and figure out what is worth building. We will ask:

— What manual process are you automating, or what product feature are you adding?
— What data do you have, in what format, and how clean is it?
— What does a successful outcome look like — and how would you measure it?
— What systems does this need to integrate with?
— What is your timeline and rough budget?

At the end of the session you leave with:
1. An honest assessment of what is feasible and where the risks are
2. A rough estimate of timeline and scope
3. A clear next step — or an equally honest "this is not the right fit" if we cannot add value

No pitch deck, no sales pressure, no follow-up spam.

Short sprints (1–2 weeks each), inside a fixed-scope contract.

We define scope and outcome upfront so there are no billing surprises. Within that fixed scope, we iterate: you see a working version early, give feedback, and we refine before the final ship. This gives you the predictability of a fixed contract with the responsiveness of an agile process.

For larger engagements we break the project into phases — each with a defined deliverable — so you can review and green-light each phase before proceeding to the next.

We do not disappear after deployment. Every engagement includes a 2–4 week post-launch period where we:

— Monitor agent outputs for anomalies and silent failures
— Tune prompts and confidence thresholds based on real traffic
— Fix edge cases that only appear in production data
— Hand over full documentation: architecture diagrams, runbooks, prompt logs, and operational notes

After the post-launch period, ongoing support and maintenance is available on a monthly retainer. We can also train your team to manage, monitor, and extend the system independently.

We are provider-agnostic and choose the right model for the job, not the trendiest one.

OpenAI (GPT-4o, o3, o4-mini) — Strong reasoning, reliable tool use, well-documented function calling. Good default for most Agent tasks.

Anthropic Claude (Sonnet, Haiku) — Excellent for long-context document processing, nuanced instruction-following, and tasks that require careful, measured responses.

Open-source models (Llama, Mistral, Phi, via Ollama or self-hosted) — For high-volume pipelines where per-token cost matters, or when data must not leave your infrastructure for compliance reasons.

In practice, most production systems use a combination: a fast, cheap model for classification and routing decisions, and a more capable model for generation, reasoning, and confidence-sensitive outputs.

Languages: Python, TypeScript / Node.js, C# / .NET
AI orchestration: LangChain, LangGraph, custom agent frameworks
Databases: PostgreSQL, Redis, Supabase, MSSQL, MySQL
Vector stores: Pinecone, Weaviate, Supabase pgvector, Chroma
Cloud: AWS (Lambda, ECS, S3, RDS, Bedrock), Azure (Functions, Cosmos DB, OpenAI Service)
Frontend: Next.js, React, Vite
DevOps: Docker, GitHub Actions, Vercel, Railway

We pick the stack that fits your existing environment. If you are already on Azure with a .NET backend, we build in that ecosystem. If you are greenfield, we recommend the fastest path to a maintainable production system.

Yes — this is the norm, not the exception. Most engagements involve wiring AI to existing systems rather than building in isolation.

Common integrations we have built:
— CRM systems (HubSpot, Salesforce, Pipedrive)
— Document stores (SharePoint, Google Drive, Dropbox, Notion)
— Communication tools (Slack, Microsoft Teams, email via SendGrid / SMTP)
— ERP and accounting software (SAP, NetSuite, Xero, QuickBooks)
— Custom REST and GraphQL APIs
— SQL databases (PostgreSQL, MySQL, MSSQL)
— Webhook-based event systems and message queues (SQS, Kafka, RabbitMQ)

If it has an API, a connector, or a database we can reach, we can wire it in. We assess your integration landscape in the scoping session.

We treat data security as a constraint, not an afterthought.

— Development uses sandboxed or synthetic data — not your production data — wherever possible
— When data must be used, we work within your cloud infrastructure so it never leaves your environment
— We do not use your data to fine-tune or train models; we use pre-trained model APIs
— For regulated industries (healthcare, finance, legal) we follow relevant data handling requirements and can deploy to self-hosted or private cloud environments
— All credentials and API keys are managed via environment variables and secrets managers (AWS Secrets Manager, Azure Key Vault) — never hardcoded
— We sign NDAs and data processing agreements (DPAs) before any work begins, if required

It means the system behaves reliably under real conditions — not just during a demo. Specifically:

Error handling: The agent handles malformed input, LLM timeouts, rate limits, and downstream API failures gracefully — retrying where appropriate, escalating to humans where not.

Observability: Structured logging, distributed tracing, and alerting so you know when something breaks before your users do.

Prompt stability: Prompts are versioned, tested with representative inputs, and evaluated against regression suites so a model update does not silently break your outputs.

Scalability: The system handles your realistic peak load, not just one request at a time in a notebook.

Deployability: It runs as a proper service in your infrastructure with health checks, restart policies, and resource limits.

Documentation: Runbooks, architecture diagrams, prompt logs, and handover notes so your team can operate and extend it.

Scope varies too much for a published rate card. As a rough guide:

— Single AI Agent (one process automated, one or two integrations): from €5,000–€10,000
— AI Agent system with dashboard and multiple integrations: €15,000–€35,000
— Custom AI SaaS product MVP: €30,000–€70,000+
— Internal copilot over your own data: €10,000–€20,000

These are starting ranges, not quotes. The actual cost depends on the number of integrations, data pipeline complexity, frontend scope, and timeline constraints.

The free consultation produces a fixed-price estimate with a clear breakdown. We price per project, not per hour — so you know exactly what you are committing to before signing.

Fixed-price for the initial build, always. We define the scope upfront, produce a spec, and deliver to that spec at the agreed price — no hourly meters running, no surprise invoices at the end of the month.

This requires a well-defined spec before we start, which is why the scoping phase exists. If scope changes materially mid-project — new integrations, new requirements that were not on the spec — we discuss a change order openly before doing the additional work.

For ongoing work after the initial delivery (maintenance, feature iterations, prompt tuning, monitoring), we offer monthly retainer arrangements.

Our smallest engagements start around €5,000. Below that threshold, the overhead of proper scoping, architecture, testing, and deployment does not leave room for quality work — and quality is non-negotiable for production AI systems.

If you have a smaller, very well-defined task — a specific API integration, a prompt engineering audit, a one-off extraction pipeline — we can scope it in the free consultation and see if it fits within a smaller engagement. The call is always free and honest.

Primarily startups from Seed to Series B and SMBs with 5–200 employees. We occasionally work with internal innovation teams inside larger organisations when the team has autonomy to move fast.

We are not the right fit for enterprise procurement cycles that take six months before a contract is signed. We work best with teams that can make decisions quickly, have a technical point of contact (or are willing to work closely with us to fill that gap), and care about measurable outcomes over extensive documentation.

Yes — many of our best clients do not have a technical background. What we need from you is not technical knowledge; it is business clarity.

Specifically: a clear explanation of the business problem you are solving, access to the relevant data or systems (with help from your team if needed), and a decision-maker who can give feedback weekly.

We translate technical decisions into plain-language outcomes. Weekly demos show you what works, not GitHub diffs. We will also make sure you understand what you are getting well enough to evaluate it, extend it, and not be permanently dependent on us.

Three differences that matter:

AI-native, not AI-adjacent: AI is not a feature we layer onto standard software projects. It is the core of every system we build. We are not a web agency that has added an AI practice; we are an AI systems company with 14+ years of production engineering behind it.

Outcome-driven, not hours-driven: Fixed scope, fixed price, measurable results. We are motivated to ship fast and well, not to bill more hours. If we can solve your problem in 3 weeks instead of 6, we do — the price is the same.

Production experience, not demo experience: Shipping AI that works in a controlled demo is easy. Shipping AI that handles real, messy production data, stays reliable over time, and is maintainable by your team is hard. Our 14+ years in custom software development means we have done the hard version.

Book a free 60-minute consultation via Calendly. No preparation needed — just write a sentence or two about your problem in the booking notes so we can come prepared with relevant questions.

After the call:
— If there is a fit, we send a technical spec and fixed-price estimate within a week
— If there is not a fit, we tell you honestly and, where possible, point you in the right direction
— Most projects kick off within 2 weeks of the spec being agreed

That is it. No RFP process, no procurement calls, no months of back-and-forth.