AI chatbots & assistants
Support and internal assistants grounded in your own documentation, with handover to a person when confidence drops.
Outcome-led
Every model ships with the metric it is supposed to move.
Your data
Retrieval over your own documents and systems, with access controls intact.
Evaluated
Test sets and review loops so quality is measured, not assumed.
Human in the loop
Escalation paths and audit trails wherever a decision carries risk.
■ What we build
From one automated workflow to a production model
We start with the task that costs your team the most hours, prove value there, then extend. No platform rebuild required.
Process automation
Document intake, classification, extraction, and routing that removes manual data entry from a workflow.
Computer vision
Inspection, counting, defect detection, and OCR on production imagery, on device or in the cloud.
Predictive models
Demand, churn, maintenance, and risk forecasting built on your historical data and delivered where decisions happen.
LLM integrations
Retrieval, tool use, and agent workflows wired into your CRM, ERP, or internal apps with proper guardrails.
Data & MLOps foundations
Pipelines, labelling, feature stores, and monitoring so models keep working after the first release.
How we work
Prove it on real data before you commit
A short feasibility phase comes first. If the data will not support the use case, you find out in weeks, not after a year of build.
01
Align
Use case & data audit
We pick the workflow with the clearest payback and check whether the data you hold can actually support it.
Use-case workshop
Data audit
Success metric
02
Prove
Prototype & evaluation
A working prototype on a slice of your real data, scored against a test set you agree with us up front.
Baseline model
Eval set
Accuracy report
03
Build
Engineering & integration
Pipelines, APIs, and interfaces so the model lives inside the tools your team already uses.
Data pipeline
API layer
Weekly demos
04
Verify
Testing & safeguards
Edge cases, bias checks, prompt and access hardening, plus the fallback path for when the model is unsure.
Edge-case suite
Bias review
Escalation rules
05
Operate
Deploy & monitor
Rollout, drift and cost monitoring, and retraining cycles, with a documented handover to your team.
Drift monitoring
Cost tracking
Retraining plan
Weekly demos, a shared backlog, and one point of contact throughout. Estimates are confirmed after discovery.
Book a discovery call
Technologies
Model-agnostic, portable by design
We keep the interface between your system and the model thin, so switching provider or hosting later is a configuration change rather than a rebuild.
Models
Hosted and open weight- Claude
- OpenAI
- Llama & Mistral
- Hugging Face
- Fine-tuning & LoRA
ML & vision
Training and inference- PyTorch
- TensorFlow
- scikit-learn
- OpenCV & YOLO
- ONNX Runtime
Data & retrieval
Grounding on your content- pgvector & PostgreSQL
- Pinecone & Qdrant
- LangChain
- Airflow & dbt
- Snowflake & BigQuery
Platform
Serving and monitoring- Python & FastAPI
- Docker & Kubernetes
- AWS & Azure
- MLflow
- On-premise hosting
Data cannot leave your network? We deploy open models on your own infrastructure.
Scope & terms
What you get, how we work together, how long it takes
No surprise line items. Scope, accuracy targets, and running costs are agreed in writing before build starts.
What you get
Every engagement ships with the same baseline.
- Deployed model or service with API access
- Evaluation set and accuracy report
- Source code, pipelines, and documented handover
- Monitoring dashboard and running-cost estimate
Engagement models
Pick the shape that fits how your team works.
- Feasibility sprint on your own data
- Fixed-scope build with milestone billing
- Retainer for tuning, evaluation, and retraining
- Embedded engineers alongside your data team
Typical timelines
Indicative only, confirmed after discovery.
- Feasibility sprint: 2–4 weeks
- Chatbot or assistant: 6–10 weeks
- Vision or predictive model: 10–18 weeks
- Estimates confirmed after discovery
Start a project
Tell us which task should get easier
Send the workflow, the data you hold, and any constraints on where it can live. You get a scoped plan and an estimate, not a sales sequence.
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