AI Consulting for Small Business & the Mid-Market
AI that pays for itself — measured in your P&L, not in demos.
We help companies with 25–2,000 employees use AI to win more work, move faster, and cut operating costs — without hiring a data science team. Every engagement starts with a baseline and ends with a measured result.
No pitch decks. A 45-minute session on your actual operations, with two or three concrete opportunities you keep either way.
Our team spent years building production AI systems at Amazon Web Services and Broadcom — the same class of systems Fortune 100 companies run on. We bring that engineering discipline to companies that can't justify a $2M AI department.
The Mid-Market AI Gap
Your competitors aren't smarter. They're just earlier.
Large enterprises have AI teams. Startups are built on AI from day one. Companies in the middle — the businesses that actually run the economy — are told to "adopt AI" with no practical path to do it. We've seen the same three failure patterns in nearly every operation we walk into:
Tool sprawl, no results
Someone bought licenses. A few people use a chatbot for emails. Eighteen months later, nothing on the P&L has changed — because tools were deployed without redesigning the workflow around them.
No one to own it
You can't hire ML engineers at mid-market salaries, and your IT generalist is already underwater. AI initiatives die because nobody with the right skills owns the outcome.
Fear of betting wrong
Vendors promise everything. The space changes monthly. So leadership defers the decision — while quote turnarounds, intake backlogs, and rework quietly compound against you.
What We Do
Four ways we engage. All of them fixed-fee. All of them measured.
AI Opportunity Assessment
A 2–3 week diagnostic of your operation. We shadow your teams, map your workflows, and quantify where AI can move a number you care about. You receive a costed, sequenced roadmap — typically 6–10 opportunities ranked by ROI and difficulty.
- Workflow and systems audit across departments
- Opportunity sizing in dollars, not buzzwords
- Build / buy / skip recommendation for each opportunity
Fixed fee. If we can't find opportunities worth at least 10x the assessment cost, we'll tell you — and refund half.
Workflow & Back-Office Automation
The fastest payback in most companies: quoting, order entry, document intake, scheduling, claims, collections, reporting. We automate the repetitive 60–80% and route the judgment calls to your people.
- Quote and proposal generation from RFQs, emails, drawings
- Document processing — invoices, BOLs, intake forms, contracts
- Customer communications, status updates, follow-ups
Typical result: 40–70% cycle-time reduction in the targeted workflow within one quarter.
Custom AI Implementation
For opportunities off-the-shelf tools can't touch: demand forecasting, predictive maintenance, dynamic pricing, dispatch optimization. Built on your existing stack — ERP, TMS, CRM, field service software — not a rip-and-replace.
- Production-grade systems, engineered by ex-AWS and ex-Broadcom builders
- Integrates with the software you already run
- Your data stays yours — no vendor lock-in by design
Every build ships with documentation, training, and a 90-day support window.
Team Enablement & Fractional AI Leadership
Systems fail when people don't adopt them. We train your staff to run and improve what we build — and for clients who want a standing capability, we serve as your part-time head of AI for a fraction of a full-time hire.
- Role-specific training for operators, not generic "AI literacy"
- Standard operating procedures and governance you can audit
- Quarterly opportunity reviews as the technology moves
Fractional leadership from 2 days/month. Cancel any quarter.
How We Work
Diagnose. Prove. Scale.
We borrowed the discipline of top-tier strategy consulting and stripped out what mid-market companies don't need: the armies of analysts, the 200-page decks, the open-ended billing. What's left is a method that gets to a measured result in weeks.
Diagnose
We baseline the metrics that matter — cycle times, cost per transaction, win rates, utilization — and identify where AI moves them most. You get a roadmap with dollar figures attached, whether or not you continue with us.
2–3 weeks · fixed feeProve
One workflow, one team, production conditions. We build, deploy, and measure against the baseline. The pilot either pays for itself or we both learn it cheaply — no seven-figure commitments to find out.
6–10 weeks · fixed feeScale
What worked gets rolled out across teams and locations, with training, SOPs, and governance so your people own it. We stay on fractionally if you want a standing AI capability without a standing AI payroll.
Quarterly · cancel anytimeClient Results
Measured outcomes, by sector.
Every figure below was baselined before work began and measured after deployment. Client names are withheld under confidentiality agreements; company profiles and results are as reported.
Freight brokers, wholesale distributors, 3PLs, and last-mile carriers run on thin margins and manual back offices. The economics of AI here are unusually direct: faster quotes win more loads, better forecasts cut spoilage and carrying cost, and automated paperwork removes cost from every single shipment.
Quoting at the speed the load board moves
Regional freight broker, Midwest US · ~$95M annual revenue
The problem: Quotes took reps an average of 4 hours to turn around — pulling lane history, checking carrier rates, emailing back and forth. By then, 1 in 3 shippers had already booked elsewhere.
What we built: An AI quoting system that reads inbound RFQ emails, prices against lane history and live market data, and drafts a quote for rep approval. Reps went from assembling quotes to approving them.
Engagement: 9 weeks to production · measured over the following 2 quarters
Forecasting demand for 4,100 perishable SKUs
Food & beverage distributor, Southeast US · 6 warehouses
The problem: Buyers ordered on gut feel and last year's numbers. The company was writing off 7.2% of perishable inventory while simultaneously stocking out on top movers during demand spikes.
What we built: A demand forecasting model blending sales history, seasonality, weather, and local events — surfaced as a simple recommended-order screen inside their existing ERP. Buyers retain final say.
Engagement: 10 weeks to production · results measured over 6 months
Taking the paper out of paperwork
Third-party logistics provider, Texas · 2.1M sq ft across 4 facilities
The problem: A 9-person back-office team manually keyed BOLs, PODs, and customs documents into three systems. Cost per shipment processed: $11.40, with a 2.8% error rate driving chargebacks and disputes.
What we built: AI document processing that extracts, validates, and posts shipment documents automatically, flagging only exceptions for human review. 83% of documents now flow through with zero touches.
Engagement: 8 weeks to production · team redeployed to client service roles, no layoffs
Fewer failed deliveries, quieter phones
Last-mile carrier, Pacific Northwest · 70 routes/day for furniture & appliance retailers
The problem: 11% of deliveries failed — customer not home, wrong window, access issues. Each failure cost ~$85 in re-delivery, and "where's my delivery?" calls consumed 60% of dispatcher time.
What we built: AI route sequencing tuned to delivery-window reliability, plus automated customer communications: confirmations, live ETAs, and rescheduling by text — no app download, no hold music.
Engagement: 7 weeks to production · NPS from retail partners up 21 points
Job shops, plants, and field-service operations share a common constraint: their most experienced people are the bottleneck. Estimators, dispatchers, and senior techs hold the business in their heads. AI doesn't replace that judgment — it multiplies how far it reaches.
Same-day quotes from a 5-day backlog
CNC machine shop, Ohio · aerospace and medical device components
The problem: Two senior estimators quoted every job from drawings — a 5-day backlog. The shop was silently turning away work because RFQs went stale before they were priced.
What we built: An AI estimating assistant that reads drawings and RFQ packages, matches them to 11 years of job history, and drafts a quote with materials, cycle times, and margin for estimator sign-off.
Engagement: 10 weeks to production · estimators now review, not assemble
Right tech, right part, first visit
Commercial HVAC contractor, Arizona · 1,900 service contracts
The problem: First-time-fix rate sat at 67%. A third of jobs needed a second truck roll — wrong parts, wrong skills match — at roughly $310 per repeat visit, plus contract penalties for missed SLAs.
What we built: AI dispatch that matches job descriptions and equipment history to technician skills and van inventory, and pre-stages likely parts based on the failure pattern described in the service call.
Engagement: 8 weeks to production · SLA penalty payouts down 78%
Hearing machines fail before they do
Injection molding manufacturer, Michigan · 38 presses across 2 plants
The problem: Unplanned press downtime averaged 14 hours per machine per month. Maintenance was purely reactive; a single failed press during a peak run once cost a $190K order.
What we built: Predictive maintenance using sensors the presses already had — cycle times, pressures, temperatures — to flag degradation days before failure, and schedule fixes into planned changeovers.
Engagement: 12 weeks to production · no new sensor hardware required
Bidding 75% more work with the same estimators
Commercial electrical contractor, Colorado · design-build and bid-spec work
The problem: Three estimators could produce ~12 bids a month, so leadership cherry-picked which RFPs to chase. Takeoffs and proposal assembly consumed 80% of estimating hours.
What we built: AI-assisted takeoff from plan sets and an automated proposal generator using the firm's own pricing library and past bids. Estimators shifted their time to pricing strategy and risk review.
Engagement: 9 weeks to production · zero estimator turnover since launch
In accounting, law, insurance, and engineering firms, revenue is hours times rate — and senior hours are scarce. The AI opportunity isn't replacing professionals; it's stripping the low-value work out of every engagement so capacity, realization, and margins all move at once.
Tax season without the death march
Regional CPA firm, North Carolina · 8,400 returns annually
The problem: Staff spent nearly half of prep time chasing, sorting, and keying client documents. Seasonal overtime was burning out staff, and the firm turned away an estimated 1,000+ returns per season.
What we built: An AI intake pipeline — clients upload documents to a portal, AI classifies, extracts, and organizes them into workpapers, flags missing items, and drafts the chase emails automatically.
Engagement: 11 weeks, deployed pre-season · seasonal overtime down 35%
Quoting commercial lines in hours, not weeks
Independent commercial insurance agency, Illinois · ~$310M premium volume
The problem: Producers re-keyed client data into 6+ carrier portals per submission. Quote packages took 2–3 weeks; deals went cold and renewals leaked to faster competitors.
What we built: AI submission intake that extracts client and exposure data from applications and loss runs, pre-fills carrier submissions, and assembles comparison-ready quote summaries for producers.
Engagement: 8 weeks to production · renewal retention up 4 points
Document review that doesn't eat the fee
Litigation-focused law firm, Georgia · commercial and construction disputes
The problem: Discovery review consumed associate hours the firm increasingly couldn't bill — clients pushed back on review-heavy invoices, and realization on litigation matters had slid to 81%.
What we built: An AI-assisted review workflow: first-pass relevance and privilege classification, chronology building, and key-document summaries — with attorney verification on everything that matters.
Engagement: 9 weeks to production · associates redeployed to depositions and strategy
Proposals from the firm's whole memory
Civil engineering consultancy, Virginia · municipal and transportation work
The problem: Every proposal was built from scratch by senior engineers — 30 to 50 unbillable hours each — even though the firm had answered most RFP questions before, somewhere, in 20 years of files.
What we built: A knowledge system over two decades of proposals, project sheets, and resumes, plus an AI proposal drafter that assembles tailored first drafts from the firm's own proven content.
Engagement: 10 weeks to production · pursuing 40% more RFPs with the same team
Why the numbers hold up.
Consulting earns its reputation for vagueness. We run every engagement against three rules that make vagueness impossible:
Baseline before build
We measure the current state — cost, time, win rate — before writing a line of code. If the metric can't be measured, we don't take the engagement.
P&L attribution
Every result is stated as revenue gained or operating cost removed, validated with your finance lead — not "efficiency" hand-waving.
Your team owns it
Success is your people running the system without us. Documentation, training, and handoff are in scope on every engagement, not an upsell.
Common Questions
What owners and operators ask us first.
No. Our typical client has 25 to 2,000 employees and no in-house data team. We design engagements specifically for companies that can't hire ML engineers — fixed fees, short timelines, and systems your existing staff can run.
Before any build begins, we baseline the metric the work is meant to move — quote turnaround, cost per shipment, first-time-fix rate, billable utilization — and agree on the target with you. Every engagement ends with a measured before-and-after, attributed to revenue increase or cost reduction and validated with your finance lead.
Almost never. We build on top of the systems you already use — your ERP, TMS, practice management, or field service platform. Most of our work connects AI to the tools your team already knows, which is also why adoption sticks.
An AI Opportunity Assessment takes 2–3 weeks. A first production deployment typically takes 6–10 weeks. We deliberately avoid long open-ended projects: every phase has a fixed scope, a fixed fee, and a measurable exit criterion.
Your team runs the system. Every engagement includes documentation, training, and a 90-day support window. Clients who want ongoing help retain us fractionally — typically two to four days a month — for a fraction of a full-time AI hire.
Your data stays yours. We architect every system so your proprietary data is never used to train shared or third-party models, and we'll put that in writing. Our team built data systems at AWS and Broadcom; enterprise-grade data handling is the default, not an option.
Start a Conversation
Book a free 45-minute working session.
Not a sales call. Bring your messiest workflow — quoting, intake, dispatch, anything — and we'll work through where AI would and wouldn't pay off in your operation. You'll leave with two or three concrete opportunities, whether or not we ever work together.
What to expect
We respond within one business day. The session is with a senior advisor — the people who built AI systems at AWS and Broadcom — not a sales rep reading a script.
Sectors we know deeply
Logistics & distribution · Manufacturing & field services · Professional services — and adjacent operations-heavy businesses.