AI strategy · engineering · forward-deployed

Ship oneAI‑nativeworkflow first.

BuildModal embeds with your team, finds the highest-leverage workflow, and ships the agent or tool that makes it run. Start small, prove the value, then expand.

See engagement options View case studies
Track record
30+Engagements shipped. Strategy only counts when the workflow runs.
Small teamsFirst workflow in 30 days
Scaling teams6‑10 week proof sprint
EnterpriseEmbedded AI pods
Proof

Built AI systems for Nike, Mr. Cooper, and Levi's. Trained teams at Meta, Salesforce, and IBM. Now packaged for teams starting with one high-leverage workflow.

What we do

Strategy and engineering, one accountable team.

We do not start with models. We start with the workflow where speed, margin, conversion, or quality can move.

01
Map
Find the first build worth shipping.

We map the workflows, data, constraints, and ROI so the first AI build is tied to a real business outcome.

  • KPI & constraint diagnostic
  • Workflow and data map
  • Use-case priority stack
  • Build plan with ROI logic
02
Ship
Build the system your team can run.

We design, build, and deploy the agent, model, automation, or internal tool into your existing stack.

  • One production workflow live
  • Human review loop
  • You own the code & the IP
  • Training and runbook handoff
Engagement model

Three ways to turn AI into operating leverage.

Pick the shape that matches your stage. Small teams get a partner who ships the first workflow. Larger teams get a scoped sprint or pod around the business outcome.

Small business & startups
AI Operating Partner
Become AI-native before you hire an AI team.
$3k-$7.5k
Monthly partner retainer

For founders and lean operators who need an AI lead plus builder before they hire a full team.

  • Operating map in week one
  • First workflow live in 30 days
  • Fractional AI lead plus builder
  • Monthly adoption and ROI review
Become a partner
Startups ready to scale
Proof Sprint
Turn one bottleneck into a reusable system.
$25k-$75k
Fixed-scope build · 6-10 weeks

For teams with one urgent bottleneck and enough signal to ship a production workflow now.

  • 1 production workflow shipped
  • Agent, model, automation, or tool
  • Deployed on your stack
  • You own the code & the IP
  • Runbook and operator training
Book scoping call
Mid-market & enterprise
Embedded AI Pod
Let's design the right pod.
Custom
Pod engagement · scoped together

For companies ready to build repeatedly around one business outcome, with governance and security in the loop.

  • Senior AI lead and engineering pod
  • Architecture, data, and security review
  • Quarterly roadmap and executive reviews
  • Build, measure, and expand by workflow
Design a pod
How it works

From messy workflow to live system in 6‑10 weeks.

Constraint
01

Find the metric.

Margin, cycle time, qualified leads, throughput, retention, or cost-to-serve. The workflow has to move a number.

Workflow
02

Map the loop.

People, systems, data, approvals, exceptions, and handoffs. This is where useful AI is designed.

System
03

Choose the pattern.

Classical ML, optimization, RAG, voice, automation, internal tool, or agent orchestration. Whatever the workflow needs.

Build
04

Ship into your stack.

No forced migration. The code, IP, and deployment stay with you.

Adopt
05

Measure and hand off.

Train operators, document the workflow, track adoption, and decide what to build next.

Case studies

Proof from systems that left the deck.

Lean teams see how the first workflow goes live. Enterprise teams see how governed systems work inside complex operations.

Small business / startup AI

Turning a lean operator into an AI-native business without hiring a full AI team

The first win was not a giant transformation program. It was a focused operating system: find the highest-leverage workflow, ship it, train the team, and turn the result into growth proof.

30 days First workflowLean Team size1 Proof asset
Read case study
Retail AI / pricing

Building a price and promotion system that moved margin and demand together

A margin problem was not treated as a dashboard problem. The work became an operating layer for price, markdown, wholesale, and seasonal campaign decisions.

+5% Net margin+10% Demand4 Decision scope
Read case study
Growth AI / MMM

Turning marketing mix modeling into an executive planning system

The goal was not another attribution report. It was a reusable planning layer that could forecast demand, allocate spend, and explain the tradeoffs behind every plan.

MMM Model familyDigital twin Planning loopSpend allocation Use case
Read case study
Financial services / operations ML

Propensity and throughput models for a lending operation that needed cleaner signal

Classical machine learning still wins when the problem is ranking, routing, and cycle-time reduction. This build improved lead quality and shortened operational crossing time.

+50% Propensity lift~2x Qualified leads31 -> 29 Cycle time
Read case study
Agent systems / evals

A multi-agent orchestration layer with evals before autonomy

The work centered on making agents cheaper, more reliable, and easier to test before they touched real customer workflows.

-30% Token usageRAGAS Eval layerTool calls Risk focus
Read case study
Bring us one workflow. We will map the data, model, agent loop, and handoff before we quote the build.View all case studies →
Start the conversation

A 25‑minute call to pick the first workflow.

BM
KJ
AM
BuildModal / Discovery
Pick the first workflow.

Bring one slow, manual, or expensive workflow. We will pressure test the use case and tell you the cleanest next step.

What we cover
  • Where the workflow is stuck today
  • What data, tools, and people it touches
  • Whether it fits a partner retainer, sprint, or pod
25 minutes
Google Meet
America/Toronto
Email us to schedule
Book directly

Tell us about the workflow.

Send a short note with the workflow you want to improve and the team size. We will reply with times for a 25-minute intro.

Email to book