AI that predicts,
classifies and sees not just chats.
We build custom machine learning — predictive models, computer vision, forecasting and NLP — for problems that need pattern recognition and prediction, not just a conversational interface. Senior ML engineers, fixed scope, models proven against real data before they ship.
180+ teams shipping with our engineers · ISO 27001-secure · NDA on request
— What we build
Four ways we build applied AI.
From a first proof-of-concept to a production-monitored model, we build the AI that fits problems needing prediction and pattern recognition.
Predictive analytics & forecasting
Models that predict demand, churn, risk or other business outcomes from historical data.
Demand forecasting
Churn prediction
Risk scoring
Computer vision
Image and video models for classification, detection and quality inspection.
Object detection
Image classification
Quality inspection
Classification & recommendation models
Models that sort, score or recommend — fraud detection, lead scoring, product recommendations.
Fraud detection
Lead scoring
Recommendation engines
NLP & text analytics
Traditional NLP for sentiment analysis, entity extraction and document classification, distinct from generative chat.
Sentiment analysis
Entity extraction
Document classification
- Featured · Proven before it ships
Most "AI-powered" features
never had a model measured "success" meant against anything.
Most “we added AI but it doesn’t seem to help” situations happen because a model was built and shipped without ever being measured against a real baseline — nobody knows if it’s actually better than the simple rule it replaced. Before we call anything done, we benchmark against a real baseline and validate on held-out data — so “it works” is a measured claim, not a hope.
Benchmarked against baseline
Validated on real data
Production-monitored
Measured, not assumed
1
SCOPE
Confirm the problem is actually solvable with ML
2
BUILD
Develop and train the model on real data
3
VALIDATE
Benchmark against a baseline, on held-out data
Outcome
We start by confirming the problem is genuinely solvable with ML — so you don’t get a model built for a problem better solved with simple rules.
14 YEARS · 300+ PRODUCTS
Built to ship, and to last.
300+
Products shipped to production
across 9 industries
14yrs
Years in business
founded 2012
95%
Engagement extension
clients renew or expand
ISO 27001
Audited data security
brief → first standup
Capabilities & stack
Boring tech that
ships on time.
We build with proven ML frameworks and evaluation practices — not the newest architecture for its own sake. Real models, benchmarked and monitored.
How we pick
01
Confirm ML is actually the right tool
Before we build a model, we check whether the problem genuinely needs machine learning — sometimes a simple rule or lookup table solves it better and cheaper.
02
Benchmark against a real baseline
Every model is measured against what it’s replacing — a simple heuristic, manual process, or the current system — so improvement is a real number, not an assumption.
03
Validate on data the model hasn't seen
Held-out test sets and cross-validation confirm the model generalizes, rather than just memorizing the training data.
04
Monitor after deployment, not just before
Model performance can drift as real-world data shifts — production monitoring catches degradation before it becomes a business problem.
ML FRAMEWORKS
PyTorch
TensorFlow
scikit-learn
XGBoost
COMPUTER VISION
OpenCV
YOLO
Detectron2
custom CNN architectures
NLP & TEXT
spaCy
Hugging Face
Transformers
NLTK
MLOPS & DEPLOYMENT
MLflow
Weights & Biases
Docker
model serving
DATA & INFRASTRUCTURE
Pandas
Spark
AWS SageMaker
GCP Vertex AI
Industries
AI development,
tuned for your industry.
Every industry has its own data, prediction problems and accuracy requirements. We bring playbooks, not generic models — and build AI around your organization’s real data on day one.
Healthcare
AI for diagnosis support and operational prediction.
- Medical image classification and detection models
- Patient no-show and readmission risk prediction
- Clinical document classification and extraction
Finance & Fintech
AI for risk, fraud and forecasting
- Fraud detection and transaction risk scoring
- Credit risk and default prediction models
- Financial forecasting and anomaly detection
Retail & E-commerce
AI for demand, recommendations and vision.
- Demand forecasting and inventory optimization
- Product recommendation engines
- Visual search and product image classification
Logistics
AI for routing, prediction and quality inspection.
- Delivery time and ETA prediction models
- Route optimization and demand forecasting
- Computer vision for package/damage inspection
Engagement models
Pick how you want to
Ship with us.
Three ways in — all senior ML engineers, all fixed against the accuracy and business-impact outcome we agree on before the first model is trained.
Fixed-scope
AI feasibility audit
Best when you’re not sure if your problem is actually solvable with ML.
- Fixed price, 3–6 weeks
- Feasibility assessment and data readiness review
- A go/no-go recommendation with approach outline
Most popular
Fixed-scope build
Best for a defined prediction, classification or vision problem.
- Scope → launch in 6–12 weeks
- Fixed scope, fixed price — no hourly drift
- Benchmarked, validated and production-monitored
- Full model documentation & handover
Embedded
Staff Augmentation
Plug senior ML engineers into your team.
- 8+ yr average, hand-picked, never on a bench
- Model development, MLOps and evaluation skills
- Direct line to your data science or engineering lead
FAQ
The questions everyone asks before kickoff.
Not sure if your problem actually needs machine learning? Book a 30-min call with a senior ML engineer.
What's the difference between this page and your Generative AI / LLM pages?
This page covers traditional applied AI/ML — prediction, classification, computer vision, forecasting — for problems that need pattern recognition, not conversation. Our Generative AI Consulting, AI Agents Development and LLM Development & Fine-Tuning pages cover the LLM/chat/agent side specifically. Many businesses need both; we help you figure out which problem you actually have.
How do we know if our problem is actually solvable with machine learning?
We run a feasibility audit — checking whether you have enough relevant historical data, whether the pattern is learnable, and whether a simpler approach (a rule, a lookup table) might solve it just as well for less cost and complexity.
How much data do we need to build a useful model?
It depends heavily on the problem — some classification tasks work with a few thousand labeled examples, while complex computer vision or forecasting problems may need much more. We assess your actual data during the feasibility audit rather than quoting a universal number.
How do you know if the model is actually good, or just seems to work?
We benchmark every model against a real baseline — the current process, a simple rule, or a naive prediction — and validate on data the model hasn’t seen during training, so “it works” is backed by a measured accuracy number, not an impression.
Can you build computer vision models, or only text/data-based AI?
Yes — computer vision for classification, object detection and quality inspection is one of our core capabilities, alongside predictive models and NLP.
What happens if the model's accuracy degrades over time?
We set up production monitoring to track model performance against real-world outcomes, so degradation (data drift) is caught and addressed with retraining, rather than discovered months later as a business problem.
Do you build models from scratch, or use existing pre-trained models?
Both — we use pre-trained models and transfer learning where they genuinely fit, which is often faster and cheaper, and build custom models from scratch when your problem needs it. We recommend based on what actually works, not by default.
Who owns the model and its training pipeline after handover?
You own it, fully — model code, training pipeline, and documentation, with no vendor lock-in. We hand over everything your team needs to maintain, retrain and extend it.
Healthcare · HIPAA-ready since 2012
Let's find out if your problem is actually solvable with AI.
Send your brief — a senior ML engineer (not a sales rep) replies within 24 hours with an honest feasibility read, and a fixed-scope plan if it’s a good fit.
- A 30-min scoping call with a senior ML engineer
- An honest feasibility and data-readiness read
- A model approach recommendation
- A benchmarked, validated model — yours to keep
