AI & Data
Turn operational data into decisions your team trusts.
We build the data layer and the models on top of it: pipelines that clean and move data reliably, machine learning that detects anomalies and predicts failures, and analytics that people actually open. Practical AI, measured against business outcomes.
At a glance
- Data engineering
- Anomaly detection & predictive maintenance
- Computer vision
- Forecasting & optimization
+ 4 more below
Where teams get stuck
The problems we are usually called in for.
Problem 01
A data lake with no fish
Terabytes collected, nothing decided. The pipeline exists; the questions were never defined.
Problem 02
Dashboards that lie
Three reports, three different numbers. Nobody trusts any of them.
Problem 03
The proof-of-concept that never left the notebook
The model worked on a laptop. Nobody built the serving, monitoring and retraining loop around it.
Problem 04
AI as a press release
Pressure to do something with AI without a use case that pays for itself.
What we deliver
Capabilities, not buzzwords.
Each of these is a thing we have shipped and supported in production. Ask us for the specifics on a call.
- 01
Data engineering
Batch and streaming pipelines (Kafka, Airflow, dbt) with schema management, quality checks and lineage.
- 02
Anomaly detection & predictive maintenance
Models trained on sensor and telemetry data to flag drift, failures and outliers before they cost money.
- 03
Computer vision
Defect detection, counting, OCR and safety monitoring on the edge or in the cloud.
- 04
Forecasting & optimization
Demand, energy and capacity forecasting; scheduling and routing optimization.
- 05
LLM applications and agents
Retrieval-augmented assistants and tool-using agents over your documents and systems, with guardrails, evaluation and cost controls.
- 06
Voice AI
Voice agents that answer and place calls: streaming speech, an LLM behind it, safe tool calls and outcomes written back to your CRM.
- 07
Analytics platforms
Warehouses (BigQuery, Snowflake, Postgres) and BI layers with a single definition of every metric.
- 08
MLOps
Model registries, serving, monitoring and retraining so models keep working after launch.
Tools we reach for
- Python
- PyTorch
- scikit-learn
- ONNX
- TensorRT
- Kafka
- Airflow
- dbt
- Spark
- BigQuery
- Snowflake
- PostgreSQL
- TimescaleDB
- Grafana
- Vector databases
How it runs
The same five steps, tuned for this practice.
Discovery first, architecture before code, weekly demos, staged deployment and SLA-backed operation.
01
Discover
Two weeks to understand the problem, the constraints and the people. We leave with a written brief, a risk register and a scope you can sign.
02
Architect
System design, security model, data flow and cost estimate, reviewed with your team before anyone writes production code.
03
Build
Weekly demos of working software. Typed, tested, reviewed. You see progress, not status reports.
04
Deploy
Staged rollouts with monitoring in place before launch. Runbooks written. Rollback rehearsed.
05
Operate
SLA-backed support, scheduled improvements and a named engineer who knows the system.
Engagement
Ways to engage this practice.
Fixed-scope project
Defined deliverables, milestones and price. Best for well-understood builds with a clear finish line.
Dedicated team
A cross-functional squad embedded with your product, billed monthly. Best for evolving products and long roadmaps.
Team extension
Senior engineers who join your existing team under your process. Best for capacity and specialist skills.
Related blueprints
Where ai & data fits in a full system.
FAQ
Questions about ai & data work.
Do we have enough data for machine learning?
Often more than you think, sometimes less. We start with a data audit and a baseline, and if a rules engine beats a model, we will tell you.
Can you run models on the device instead of the cloud?
Yes. We optimize models for edge hardware (ONNX, TensorRT, TFLite) where latency, bandwidth or privacy require it.
How do you measure whether AI is working?
Every model is tied to a business metric (false positives per week, downtime avoided, hours saved) and monitored against it in production.
Do you build LLM and generative AI products?
Where they solve a real problem, yes: internal assistants, document processing and support automation with evaluation and guardrails.
Is our data safe?
Data stays in your environment. We work inside your cloud accounts, under your access policies, with encryption and audit trails.