AI for the Way Your Business Actually Runs

Enterprise AI Workflows

Predictive maintenance, demand forecasting, fraud detection, computer vision QC, dynamic pricing — engineered into your operations stack with measured ROI.

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The most valuable AI work isn't a chatbot. It's the system that catches a $200K/month manufacturing failure two weeks before it happens, the model that flags the fraudulent invoice before it gets paid, the computer vision pipeline that catches defective product before it ships. We build those systems for mid-market and enterprise clients across manufacturing, healthcare, finance, and logistics.

Every engagement starts with a real business case — not a 'let's explore AI' workshop. We measure the current cost of the problem, design the smallest system that moves the metric, and ship it in weeks, not quarters. Then we expand from there.

Why teams hire us for this

The four things you'll get from a Niche.dev engagement on enterprise ai workflows.

ROI-first scoping

Every project starts with the dollar value of the problem you're solving. If we can't make the math work, we'll tell you before we touch a keyboard.

Engineering rigor, not consulting decks

You get working systems in production, not 60-page strategy documents. We're engineers who happen to specialize in AI, not consultants who heard about it.

Build vs. buy honesty

If an off-the-shelf product fits your need, we'll tell you. We only build custom when the ROI math justifies it — usually 3–5× better than the SaaS alternative.

Operate after we build

Optional managed-service tier. We monitor, retrain, and evolve the system as your business changes. Most clients keep us on for 12+ months after launch.

What we build

Concrete systems we've shipped in this space. Not a roadmap — production deployments.

Real numbers, real production

Aggregate metrics from Niche.dev enterprise ai workflows deployments.

$200K+

Avg. monthly downtime caught by predictive maintenance

60×

Faster fraud detection vs. manual review

3–5×

Better ROI than off-the-shelf SaaS alternatives

8 wk

Avg. time from kickoff to production pilot

How an engagement runs

Predictable, milestone-based, no open-ended retainers. You see real progress every two weeks.

Business case

We quantify the dollar size of the problem and the realistic upside. If a project doesn't clear a 3× ROI bar in year one, we don't recommend it.

Data and feasibility

Two-week deep dive into your data quality, system access, and signal richness. You get a written feasibility report before committing to a build.

Pilot system

Four- to eight-week build of a production-grade pilot, deployed in shadow mode against your real operations data. You measure the lift before going live.

Production rollout and ops

Phased cutover, monitoring dashboards, and an optional managed-service contract for ongoing model updates and capability expansion.

The stack we work in

We bring opinions but we meet you where you are. These are the tools we use most for enterprise ai workflows.

Python PyTorch / TensorFlow scikit-learn / XGBoost OpenCV NVIDIA Triton / TensorRT AWS / GCP / Azure Snowflake / BigQuery / Databricks Kafka / Kinesis Airflow / Prefect Grafana / Datadog

Real enterprise ai workflows we've shipped

Every one of these is a production system. Click through for the full case study.

Frequently asked questions

The questions every prospect asks before working with us.

We've tried AI consultants before and got slideware. How are you different?
We're engineers, not consultants. Every engagement ends with a working system in production, not a strategy document. You can verify by looking at our case studies — every one is a system we shipped, with the actual metrics it delivered.
What size company do you work with?
Mid-market to enterprise, typically $20M–$2B in revenue. Below $20M, the engagement size usually doesn't justify a custom build (we'll point you to off-the-shelf options instead). Above $2B, we partner with internal teams rather than replacing them.
How does pricing work?
Fixed-fee for the initial build (typically $40K–$200K depending on scope), with optional monthly managed-service fees ($5K–$25K/month) for ongoing operation. We share the proposal with the ROI math worked out so you can see the payback timeline.
Do you handle the ongoing operation or do we?
Both options. We can hand off documentation and runbooks for your team to operate, or keep operating it ourselves under a managed-service contract. Most clients start managed and graduate to in-house after 12 months.
What if our data is messy?
It always is. The data audit is part of the feasibility phase. If the data is too sparse or low-quality for the planned model, we'll flag it before you commit — and often we can recommend instrumentation work to fix it before the AI build starts.

Have a real operational problem to solve?

Tell us what it's costing you today. We'll send back a 1-page system sketch, a feasibility plan, and the ROI math within 48 hours — and we'll tell you if AI isn't the right answer.

Email Nick directly