B2B Services
AI inside the operations of services businesses — back-office automation, knowledge work, sales operations.
B2B services companies (consulting, agencies, professional services, B2B SaaS) have the highest leverage from AI today. Most of the value is in compounding knowledge work — surfacing the right information, automating repetitive operations, and amplifying senior teams.
Where AI fits
High-leverage use cases in this industry.
01
Internal knowledge systems
RAG over your past projects, methodologies, and operational knowledge. Junior team members get senior-quality context. Senior team stops repeating themselves.
02
Sales operations automation
Lead qualification, account research, proposal drafting, CRM hygiene. Agents that operate inside your sales stack, with the human in the loop where it matters.
03
Back-office workflow automation
Document processing, contract review, invoice handling, compliance checks. Concrete throughput gains in operations functions.
04
Customer-facing assistants
Support copilots, onboarding assistants, in-app intelligence. Built into your product, measured on customer outcomes.
How we approach this industry
A pattern that respects b2b services realities.
- Assess01
Industry-aware discovery
Two-week sprint scoped to your operational reality — regulatory, OT/IT, integration constraints baked in from day one, not discovered mid-build.
- Design02
Reference architecture
Topology that fits your facility, your cloud, your compliance. On-prem, hybrid, or cloud — the call is documented before we start building.
- Build03
Pilot on one site / line / product
Working system on one part of your operation. Measurement window with explicit success criteria. Go/no-go decision before scaling.
- Operate04
Roll out & operate
Multi-site rollout if pilot succeeds. Central observability, drift detection, runbook. Optional retainer for ongoing operations.
Frequently asked
Industry-specific questions.
We have NDAs with our clients. Can their data be used for AI features?
Depends on the NDA. We start every B2B services engagement by reviewing the data governance picture and identifying what's usable, what isn't, and what needs renegotiation. Sometimes the answer is 'this feature is only available to clients who opt in' — that's clean.
How do we measure adoption inside our team?
Defined during discovery. Common metrics: weekly active users per team, queries-per-user-per-week, task completion rate via the AI vs the old workflow. Adoption is engineered, not assumed.
Can this be a competitive advantage if our competitors can buy the same tools?
Off-the-shelf tools are commoditised. Custom AI built on YOUR data, YOUR processes, and YOUR clients' context is harder to replicate. The compounding advantage comes from the data flywheel, not the model.
Do you work with venture-backed scaleups or only enterprise?
Both. Our sweet spot is 100–2000 employees, post-Series B SaaS or established services firms. We've worked with smaller and larger but the engagement shape we run best fits this range.