Industry · Software companies, SaaS, platforms
AI agents in your product and in the company that ships it.
Software companies meet AI twice. Customers expect agentic features, copilots and an MCP server so their own agents can use your product, and your own teams are expected to run on AI in engineering, support and sales. We help with both, with the discipline that keeps LLM costs, quality and EU obligations under control.
Where AI pays off for software companies
Adding a chat box to your product is easy. Shipping an agent that customers trust, that scales with margin intact and that survives an enterprise security review is the actual work.
Most SaaS teams have already run an experiment: a chatbot over the docs, a summarisation feature, a copilot beta. What follows is harder. Agentic features need evaluation suites, cost controls, tenant isolation and a story for the procurement team of your largest customer. In parallel, the company itself is expected to become AI-native, from coding agents in engineering to support deflection and onboarding agents.
We work with product and engineering leaders on both sides: designing and building agentic features and MCP servers inside the product, and setting up the AI-assisted operations that let a lean team serve more customers. The common thread is engineering discipline. Evals before launch, tracing in production, gates where humans review, and a cost per interaction that fits your pricing.
We work with B2B software companies, SaaS and platform businesses in Germany, Austria, Switzerland and wider Europe, from seed-stage startups to established vendors with enterprise customers, in English and German. We are comfortable in your codebase and in your board meeting.
- In the productCopilots, workflow agents, MCP servers, AI features
- In the companyCoding agents, support, onboarding, sales engineering
- Operating principleEvals before launch, humans at the review gates
- First stepReadiness assessment, or a 6–8 week pilot
Use cases for software and SaaS companies
Seven ways software companies put agents to work, in the product and behind it.
Agentic features inside the product
Copilots that answer and act in context, workflow agents that complete multi-step jobs for the user, and an MCP server so your customers' own agents can operate your product through a standard interface. We design the feature, the guardrails and the pricing model together. Read MCP explained.
AI-assisted engineering
Coding agents that write, refactor, test and document under a review process: branch protection, automated checks, human review gates and clear rules on what an agent may merge. Faster delivery without turning the codebase into something nobody understands. See IT and engineering.
Support deflection and technical support agents
An agent over your docs, changelogs, tickets and known issues that resolves the routine and hands the rest to an engineer with a prepared summary, logs and reproduction steps. Measured on resolution and customer satisfaction, not on deflection alone.
Onboarding and customer success agents
Agents that guide new accounts through setup, detect stalled onboarding from product data, draft the check-in and prepare the notes for the quarterly review. Customer success managers spend their time in conversations, not in spreadsheets. See customer service.
Sales engineering and RFP responses
First drafts of RFP and RFI answers from your approved knowledge base, with sources and flags wherever the honest answer is "not yet". Sales engineers review and refine instead of starting from a blank page. Read more under AI agents for sales.
Product analytics summaries
Weekly digests of usage, funnel changes, churn signals and feature adoption, written for product managers and leadership with the underlying queries visible. Fewer dashboards nobody opens, more decisions with context.
Security questionnaire automation
Enterprise customers send questionnaires with hundreds of questions. An agent answers from your policies, previous responses and SOC 2 or ISO 27001 documentation, marks anything uncertain, and your security lead reviews before anything leaves the building.
Constraints specific to software and SaaS
Six things that separate a feature demo from a feature you can sell.
LLM costs and margin
Every agentic interaction has a variable cost, and a feature that delights users at a loss is not a feature. We model the cost per interaction early, choose model tiers per task, cache and route where possible, and design the packaging so that usage-based costs are reflected in usage-based pricing.
Evals and quality at scale
What works for ten beta customers breaks at a thousand. We build evaluation suites from real usage, run them on every prompt and model change, and put tracing in production so regressions are seen before customers report them. Quality becomes a number the team owns.
Multi-tenant data isolation
Agents with retrieval and tools must never see another tenant's data. We design tenant-scoped retrieval, permissions carried into every tool call, and tests that try to break the isolation. Prompt injection through customer-provided content is treated as a security issue, not a curiosity.
SOC 2 and ISO 27001 expectations
Your enterprise customers will ask how AI features fit your existing controls: data flows, sub-processors, retention, access. We document the AI components so they slot into the control framework you already maintain, and we keep the list of model providers short and defensible.
EU customers and EU hosting
European customers increasingly require that prompts and data stay in the EU or on their own infrastructure. We build with model endpoints and vector stores that can be deployed in the EU, and we keep the model layer swappable so a hosting requirement does not force a rewrite. See sovereign AI and EU hosting.
EU AI Act obligations for providers
When you ship AI features, you may be a provider or deployer under the EU AI Act, with transparency duties, documentation and, for some use cases, high-risk obligations. Most SaaS features fall outside the high-risk list, but the classification has to be done and written down. We prepare it with you; your counsel confirms it.
How we start with software companies
Product and operations review
We look at your roadmap, your codebase, your support and onboarding data and your cost structure, and identify where an agent creates customer value and where it saves your team the most time.
Design the feature or the workflow
Scope, user experience, the tools the agent may call, guardrails, evaluation criteria, cost model and the EU AI Act classification, written down before anyone opens an editor.
Build with your team
We build alongside your engineers, in your stack, with evals and tracing from the first commit. For product features we work toward a beta with real customers; for internal agents, toward live operation with one team.
Launch, measure, hand over
Go-live with monitoring and cost dashboards, a review of the first weeks of real usage, and a handover so your team owns the system. We stay available for managed AI operations or the next feature.
Frequently asked questions
Should we build agentic features ourselves or work with you?
Both, in sequence. Your engineers know the product; we bring the patterns for evals, tool design, guardrails, cost control and MCP that take teams months to learn by trial and error. We typically build the first feature with your team and hand over a working setup, tests and documentation, so that the second feature is yours alone. See build vs. buy.
Which models should a SaaS product use?
It depends on the task, the data, the cost and where your customers are. Many products run a mix: a strong model for reasoning-heavy steps, smaller or open-weight models for classification and extraction, EU-hosted endpoints for regulated customers. We keep the model layer behind an abstraction so you can switch when prices or requirements change, and we are vendor-neutral.
What does an MCP server give our customers?
A standard way for their AI agents, whether in Claude, ChatGPT, an IDE or an internal platform, to use your product: read data, trigger actions, complete workflows, with authentication and permissions you control. It turns your product into something agents can operate, which is quickly becoming a buying criterion. Our MCP article goes deeper.
How do we keep LLM costs from eating the margin?
Measure the cost per interaction from the first prototype, choose model tiers per step, cache repeated context, limit tool loops, and design the pricing tier so that heavy usage pays for itself. In practice most cost problems come from one or two unbounded steps that nobody noticed; tracing finds them.
Does the EU AI Act apply to a normal SaaS feature?
Possibly, in a light form. Shipping an AI feature can make you a provider with transparency and documentation duties. Most SaaS use cases are not in the high-risk categories, but some, for example in HR or credit, are. We prepare a written classification and the documentation with you and recommend confirming it with legal counsel, because guidance is still evolving. Our EU AI Act guide is a good start.
Related
Custom AI agent development
Single- and multi-agent systems designed and built for production: tool use, memory, MCP servers, evaluation suites, guardrails and EU deployment.
AI agents for IT & software engineering
Service-desk agents, AI-assisted software delivery with review gates, incident triage and the integration layer that lets every other department's agents work.
MCP explained for decision makers
What the Model Context Protocol is, why it matters for connecting agents to your systems, and how to adopt it without losing control.
Build or buy AI agents?
Platform agents, low-code orchestration or custom frameworks: a comparison, the cost dimensions that matter, and the hybrid architecture we usually recommend.
Let's find the first workflow worth automating.
A 30-minute intro call, no slides and no obligation. We listen, ask about your processes, and tell you honestly where AI agents would pay off and where they would not.