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Our Approach

Custom AI infrastructure, built like infrastructure should be.

One team across scope, build, and launch. Senior throughout. Live in four to eight weeks, integrated with your stack, owned by you.

Why we work this way

AI engagements typically split. Strategy lives with one firm. Build lives with another. Launch lives with a third. The work passes through several sets of hands, and the original intent erodes in the handoffs.

Aisle is built around the opposite shape. The team that diagnoses the operating problem writes the scope. The team that writes the scope builds the system. The team that builds it carries it through to live in your business.

The engagement

Three legs. Three deliverables. One team carrying it through.

Leg 01

Scope.

15 min call → 72h written scope

  • Founding partner walks the operating problem with you
  • Diagnose what AI can move and what it cannot
  • Written scope: what we'd build, cost, timeline, lever
  • Yours to keep, whether you proceed or not

Deliverable

Written scope

Run byFounding partner

Leg 02

Build.

4 to 8 weeks

  • Architecture under partner direction
  • Engineers from OpenAI and Anthropic write the code
  • Full test coverage, regulated standards where they apply
  • Nothing subcontracted, nothing offshored

Deliverable

Custom AI infrastructure

Run byPartner with engineering

Leg 03

Launch.

In parallel with build

  • Integration with existing stack and workflows
  • Where customer facing: landing pages, paid acquisition
  • Monitoring and incident response from day one
  • Client owns the code at the end

Deliverable

Live system, GTM running

Run byPartner with growth team

What we believe

Five positions we hold.

  1. 01

    Most AI engagements fail in the handoff, not the build.

    The technical build is the easy part. The intent of the system erodes when strategy hands to engineering, when engineering hands to deployment, when deployment hands to growth. We carry it across the whole arc because that is where the failures live.

  2. 02

    The scope is the most important deliverable.

    A bad scope produces a working system that solves the wrong problem. A good scope makes the build mechanical. Most of the value of the engagement is decided in the first seventy two hours, before a line of code exists.

  3. 03

    Live is the only deliverable that matters.

    A demo is not a system. A pilot is not a system. A staging environment is not a system. A system is software running in your business every day, against real users, with real consequences. Anything short of that is unfinished work.

  4. 04

    Capacity should be bounded by what we can deliver.

    Most firms grow by adding bench. We grow by adding partners. The number of engagements we run concurrently is capped by what the partner team can carry without subcontracting. When capacity is full, we say so.

  5. 05

    The right answer is sometimes not to build.

    If the operating lever is too small, if the data is not there, if the problem is better solved without AI, we say so on the call. A clear no is more valuable than a six week build that should not have started.

What we actually build

Patterns we have built and run live.

Custom AI infrastructure is not one shape. The four patterns below cover the shape of most engagements we run. The stack we reach for is named, not implied.

Pattern 01

Retrieval augmented systems

Domain-specific knowledge bases connected to a model so it answers from your data. Used for compliance review, internal search, and customer-facing assistants where accuracy on private data is the requirement.

Anthropic APIpgvectorPostgreseval harness

Pattern 02

Agentic workflows

Models that take multi-step action against your tools and APIs. Used to replace operational team capacity, automate workflows that span systems, and run scheduled tasks that previously required human attention.

Anthropic APIOpenAI APITool useWorker queues

Pattern 03

Compliance and risk systems

AI built into regulated workflows where the cost of failure is regulatory. Document review, transaction monitoring, controlled retrieval. Built to PCI, FCA, and equivalent standards where they apply.

PCIFCAAudit loggingEval harness

Pattern 04

Customer facing AI products

AI as the product, not behind the product. Conversational interfaces, generative UI, AI-native onboarding flows. Built end to end with the GTM running in parallel so the product enters market the day it goes live.

Streaming UIReal-time evalFeature flagsGTM stack

Speak with the team.

Engagements are scoped privately. The team replies within one business day.

Book a Scoping Session→