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What we'd build for your industry.

Curated industry × service combinations. The patterns that ship most often in each vertical, with honest timelines and budget envelopes. Not every combination — the ones we'd recommend first.

Don't see your combination? Book a 20-min call → and we'll scope something for you.

The signals that say a combination is ready

Each of these pairings has a set of conditions that make it worth funding. They're specific on purpose — if none of the lines under a combination sounds like your operation, that build will probably underdeliver no matter how well it's executed.

AI chatbots for e-commerce

  • 200+ inbound support tickets / week, 60%+ are repeat questions
  • PDP conversion stalls because customers can't find a clear answer
  • Support team works weekends because the queue never empties

AI agents for B2B SaaS

  • Inbound capacity outruns SDR bandwidth
  • Engineering interrupts on tier-1 bugs are killing focus time
  • Onboarding asks the same 30 questions across customers and a docs site no one reads

AI agents for manufacturing

  • Plant data is rich but siloed across SCADA, ERP, MES, operator log sheets
  • Reactive maintenance — supervisors scramble after machines go down
  • Shift handoffs lose context every 8 hours

AI strategy for finance

  • Compliance team is asking for an AI governance policy you don't have
  • Multiple AI vendor pitches are sitting in your inbox unscored
  • Engineering wants to ship something AI-related but legal is blocking

AI chatbots for healthcare

  • Intake takes 15+ minutes of clinician time before the visit
  • Documentation eats clinician evening time
  • Patient FAQ calls outpace the staff that could answer them

AI automation for logistics

  • Document mountains (BOLs, customs, invoices, PODs) eating ops time
  • Customer-success hours burned on exception handling
  • Shipper inbound: 'where is my shipment?' dominating support volume

How to read the timelines and budgets

The timeline is for v1, not for “finished”
Every range here describes getting a working system into production against real traffic. AI systems are tuned after launch, not before it — the eval suite grows as production surfaces cases nobody predicted. That's why several of these pair a sprint with a retainer rather than pretending the work ends at handover.
The budget range is mostly about integration surface
The spread between the low and high end of each range is rarely about model sophistication. It's how many systems the build has to touch, how clean the data in them is, and how much of your process was never written down. A single-system build with good documentation lands near the floor.
Combinations overlap, and that's usually cheaper
Retrieval built for a support chatbot is the same retrieval a sales agent needs. If two of these describe you, say so on the call — sequencing them as one engagement generally costs less than two separate ones.
What every one of them ships with
Code, prompts, and IP transferred to you; human-in-loop checkpoints on anything irreversible; and logged, replayable decisions so the system can be debugged months later by someone who wasn't there when it was built.