AI Powerhouse
by INSART.
We engineer AI into how industrial businesses already run today — to cut costs, boost output, and tighten control of what's happening on the floor.
The shift
In 18 months,
AI moved from experiment to expectation.
of industrial leaders now report active AI deployment in at least one production workflow — up from 23% in 2024. The companies that win this decade aren't the ones with the best AI strategy. They're the ones that shipped AI into operations first.
Source: McKinsey Global Survey on AI · 2026
The gap
Most industrial businesses are
stuck at the pilot stage.
Vendors sold them dashboards. Consultants sold them strategy decks. Both stopped before AI actually changed how the floor runs. INSART picks up where they leave off.
"We have data. We can't act on it."
Investments in BI tools and IoT sensors that produce reports nobody reads — and certainly don't change Tuesday's production schedule.
"We have a strategy. Nothing shipped."
Six months and $400K spent on a roadmap from a Big Four firm. Zero AI in production. Operations team never met the consultants.
"We bought the tool. It doesn't fit."
A best-in-class AI platform that can't read the MES, won't talk to the ERP, and treats every plant the same. Shelfware in 90 days.
Who we are
33 years building software
that runs real businesses.
INSART has spent three decades shipping production systems for some of the most regulated, demanding industries on earth. Our fintech work runs payments, lending, and trading at scale. We brought that same engineering discipline to industrial AI.
- Founded 1993 · US-incorporated, Boca Raton FL · ~80 engineers across Ukraine, Poland, Romania, and Uruguay
- Long-term partners · Franklin Templeton, iCapital, Accel KKR portfolio companies, Bonterra, Equifax
- Built on AWS, Anthropic, Google Cloud · official partner status with the platforms we deploy on
- $150M+ raised by INSART-built portfolio companies — we know how to ship things investors and operators trust
The opening offer
The AI Plan.
Free. 4 pages. 5 days.
A focused, no-fluff document that maps exactly where AI pays for itself in your operation — and what it would take to ship it. Built specifically for your business, not a templated “industry brief.”
Where the money is
Top 3 AI opportunities in your operation, ranked by 12-month ROI and implementation risk.
What we'd build
Concrete architecture for the highest-leverage opportunity — system diagram, integrations, data sources.
How we'd ship it
Phased rollout plan, team composition, week-by-week milestones to first production pilot.
What it costs
Honest budget bands, risks called out, and what success looks like at 30 / 90 / 180 days.
No NDA required to receive it. No commitment after. If we're not the right fit, you keep the plan.
What we do
Three things. Done well.
We don't sell “AI transformation.” We do the three things that turn AI investments into operational outcomes.
Identify
Walk the floor, talk to operators, sit in the war room. We find the workflows where AI moves the needle — and the ones it doesn't.
Workflow mapping · Data audit · ROI modeling · Risk scoring
Build
Production-grade engineering, not POCs. Vision systems, predictive models, agent workflows that ship and stay shipped.
Computer vision · Predictive ML · LLM agents · Workflow orchestration
Integrate
Wire it into your MES, ERP, WMS, SCADA, telemetry — wherever the data and the decisions actually live. No shelfware.
SAP / Oracle / NetSuite · MES / SCADA · Custom legacy · API + event streams
How an engagement runs
From first call to first pilot in 9 weeks.
We don't start with research phases that bill for months. We bring engineers into your operation in week one and ship production code by week nine.
Discovery
Site visit, system access, and data audit. We meet operators on shift, not just executives in conference rooms.
Plan & align
4-page proposal becomes a signed scope with named integration points, success metrics, and stage gates.
Build
Engineering team embedded with your data and your stack. Weekly demos. Monthly steering reviews.
Pilot & scale
First production pilot live on one line / one site / one fleet. Then we scale across the rest of the operation.
Capabilities
What we ship into production.
Not a theoretical capability list. These are systems we have running today across our portfolio.
Computer vision
Defect detection, inventory ID, safety monitoring. Edge or cloud, real-time or batch.
Predictive maintenance
Vibration, thermal, acoustic, telemetry → ranked work queue for mechanics.
Quality & QC
Inline vision QC, statistical control, automated batch traceability for audits.
Demand & planning
Forecasting, S&OP automation, dynamic routing, inventory optimization.
Agent workflows
LLM-powered automation: tickets, reports, exception handling, supplier comms.
Routing & ops
Fleet routing, pit routing, line scheduling — anywhere a sequencing problem lives.
Data & integration
ETL pipelines, MES/ERP/SCADA bridges, event streams, and the boring plumbing that makes the rest work.
Intelligent automation
RPA + ML hybrid for back-office workflows: invoicing, claims, compliance, scheduling.
Operator-facing tools
Mobile, tablet, and floor-display interfaces designed for shift workers, not analysts.
The stack
Best-in-class platforms,
fit-for-purpose to your stack.
We are platform-agnostic with strong opinions. We pick what works for your data residency, latency, and compliance — not what wins us a partner discount.
Anthropic Claude · OpenAI · Mistral · Google Gemini · Open-source (Llama, Qwen) for on-prem.
YOLO · Roboflow · OpenCV · Custom PyTorch · Edge inference on Jetson / Coral.
PyTorch · scikit-learn · LightGBM · Polars · DuckDB · Databricks · Snowflake.
AWS (primary partner) · Google Cloud · Azure · On-prem & air-gapped where required.
LangGraph · Temporal · Airflow · Dagster · Custom event-driven pipelines.
Next.js · React Native · Flutter · FastAPI · NestJS · Custom legacy bridges.
SAP · Oracle NetSuite · Microsoft Dynamics · Plex · Ignition · Custom MES.
Datadog · Grafana · Sentry · Custom drift / model monitoring dashboards.
Industries
Operations-heavy businesses
where AI moves the bottom line.
If your business has plants, fleets, fields, or facilities — and margin comes from execution, not branding — we know your pain.
"INSART's team didn't ask us what AI we wanted. They asked where we lose money on a Tuesday. That's the difference."
VP Operations · North American steel processorCase study · 01 of 03
Four plants. One truth
for every ton on the floor.
AI-assisted coil ID, yard reconciliation, exception routing
A North American steel service center stopped fighting spreadsheets and reclaimed floor time. Computer vision + ERP automation across four plants — planners now trust the WMS.
Case study · 02 of 03
320+ lines.
One ranked queue for mechanics.
Vibration, temperature, MES → one prioritized work queue
Multi-line manufacturer reduced unplanned downtime and stopped firefighting. Mechanics now work from a model-ranked priority list, not a noisy alarm board.
Case study · 03 of 03
Regional LTL fleet.
Live-traffic routing that holds up to dock reality.
AI-sequenced routes vs traffic, dwell time, dock constraints
A regional carrier replaced static manifest planning with adaptive sequencing. Fewer missed windows, less fuel, calmer dispatchers.
Outcomes
What an INSART engagement
actually delivers.
Composite outcomes from the case studies above. Your numbers will be different — but the pattern of savings, speed, and adoption holds.
Before INSART
- Dashboards nobody acts on; KPIs lag reality by days
- Investments in AI tools that don't fit existing systems
- Strategy decks from consultants — no shipped code
- Operators still rely on tribal knowledge and spreadsheets
After INSART
- AI in production on the floor — not in pilot purgatory
- One source of truth across MES, ERP, and operator tools
- Decisions made in minutes that used to take days
- Engineering team trained to extend and own the system
Engagement model
Two ways to start.
One way to scale.
Most engagements start with a focused pilot to prove ROI on one workflow. From there, we scale across plants, lines, or regions.
- 2x discovery interviews with your operations leadership
- Async review of available data & system documentation
- Top 3 AI opportunities ranked by ROI
- Architecture & rollout sketch for #1
- Honest budget bands · no NDA required
Goal: a clear go / no-go on the highest-leverage opportunity.
- Embedded engineering pod (3 – 5 senior engineers)
- Architecture, build, integration, and pilot deployment
- Weekly demos · monthly steering reviews
- Operator training & runbook handover
- Fixed-fee with named milestones — no T&M surprises
Goal: one workflow live in production with measured ROI.
Scale phase (post-pilot) is scoped against the actual deployment surface — typically $400k – $1.5M / year for multi-site rollouts.
Why INSART
Engineering shop, with the
discipline of a consulting firm.
Most teams pick one or the other. We deliberately built the firm to be both — strategic enough to scope honestly, technical enough to ship.
Next steps
Where would AI
pay for itself in your operation?
Two ways to find out — both start the same call.