AI frees up the most expensive hours at service firms

Proposal drafting, knowledge management, and project steering tie up your most experienced people's time at B2B service firms. We build agents that target exactly that.

AI levers at B2B service firms

B2B service firms sell expertise by the hour. Every hour a senior employee spends on proposal drafting, research, or internal knowledge management instead of billable work costs margin directly. At the same time, every project and every new hire adds to the effort of making knowledge from past projects findable again. That limits how fast a service firm can grow without diluting delivery quality.

We apply AI where knowledge already exists but is hard to find: in project documentation, proposals, contracts, and internal guidelines. An agent with access to these sources drafts proposals and research that a senior employee reviews instead of writing from scratch. At service firms that build software or platforms for clients themselves, Agentic Engineering also adds development capacity without growing the team proportionally. The result shows up in utilization, proposal turnaround time, and the number of projects a team can carry at once.

Functions with the largest lever

  1. 1

    Proposal and knowledge management

    An agent with access to past proposals and project documentation drafts new proposals for review

  2. 2

    Customer service

    Agents with access to contract and project status answer recurring client questions directly

  3. 3

    Marketing & content

    Trade articles, case studies, and proposal materials come from project knowledge instead of starting from zero

  4. 4

    Software development

    Agentic Engineering raises delivery capacity for client projects without proportional headcount growth

  5. 5

    Finance & reporting

    Automated analysis of time tracking and project margins by client and team

  6. → Utilization

Typical use cases

  • Proposal drafting from project knowledge

    An agent searches past proposals and project documentation and drafts an initial version for review.

  • Internal knowledge search

    Employees find answers from the project archive and guidelines through a question instead of a folder structure.

  • Technical due diligence as a service offering

    Service firms that assess or hand over platforms themselves use structured approaches from our own due diligence practice.

  • Client service agent

    Answers status questions on ongoing projects directly from the project management system.

  • Agentic Engineering for client projects

    Development teams building for clients work with an AI-native toolchain and deliver more without proportionally growing the team.

Prerequisites

Data

Requirement: Project documentation, past proposals, and guidelines in a searchable place

Typical state: Usually exists, but spread across drives, email, and individual project folders

Systems

Requirement: Project management and document storage with API access

Typical state: Often in place; permission and confidentiality boundaries between client projects need to be mapped cleanly

Organization

Requirement: One owner who decides which knowledge an agent may use

Typical state: Often unresolved between project leads, management, and individual consultants

Our approach at B2B service firms

We start with the question of what knowledge already exists in the company but isn't reused because it can't be found. That produces the first use case, usually proposal drafting or internal knowledge search, because the effect on utilization shows up fastest there. Confidentiality between client projects is part of the architecture from the start, not an add-on. Where client projects are software development themselves, we also check whether Agentic Engineering directly raises the team's delivery capacity.

Real estate service provider

Ausgangslage

Real estate service provider without a documented technical picture and without a fixed stack.

Umsetzung

Tech DD report, greenfield target architecture, and a concrete tech stack decision.

Ergebnis

The company has a documented foundation and a fixed stack to build on.

Digital consultancy, Hamburg

Ausgangslage

Digital consultancy needed external architecture expertise for three parallel client projects.

Umsetzung

Tech DD of an e-commerce architecture, greenfield CMS concept, architecture for a credit platform.

Ergebnis

The consultancy could continue all three client projects on a reliable technical foundation.

Enterprise commerce platform, Hamburg

Ausgangslage

Enterprise commerce provider with a tightly coupled architecture that had grown over time.

Umsetzung

Headless architecture with Kafka event streaming and a staged AWS migration.

Ergebnis

The platform runs decoupled, with Kafka as the backbone instead of a central monolith.

Frequently asked questions

Initial call: 30 minutes, concrete.

We look at your proposal and knowledge processes and tell you where an agent frees up hours.