Predictive Models
- Classification: lead, churn and risk scoring
- Regression: pricing and propensity
- Forecasting: demand and staffing (time-series)
Production AI: Custom ML, RAG & Measured MLOps
We build AI that earns its keep—classification, regression, forecasting, NLP, computer vision and retrieval-augmented generation—backed by evals, guardrails and clear SLAs.
Models That Move KPIs
RAG Over Your Data
Guardrails & Compliance
Built for teams that care about reliability over hype
- Ops Automation
- Risk & Compliance
- Docs & Knowledge
- Healthcare & Clinics
- B2B & SaaS
USE-CASES
Where AI Pays For Itself
Swipe to browse
Need a customer-facing assistant instead? See our Chatbots service
IDEAL FOR
Who This Service Is For
- A specific, measurable decision or workflow you want automated—not “some AI”
- Existing data worth modelling: records, documents, images or event history
- Willingness to define success metrics before a model is built
- Need for evals, monitoring and cost controls rather than a one-off demo
- Compliance or privacy constraints that require PII handling and audit trails
Perfect fit
5- A specific, measurable decision or workflow you want automated—not “some AI”
- Existing data worth modelling: records, documents, images or event history
- Willingness to define success metrics before a model is built
- Need for evals, monitoring and cost controls rather than a one-off demo
- Compliance or privacy constraints that require PII handling and audit trails
Not right for
3- Exploratory research with no target metric or downstream decision
- Very small or unlabelled datasets with no path to gathering more
- Expectations of full autonomy where a human review step is legally required
Not sure whether your data can support the use-case? That’s the first thing we assess—and we’ll say so plainly if it can’t.
Ask for a feasibility readOUTCOMES
Outcomes You Can Expect
<1s
Median latency
Cached, batched and streamed inference paths
100%
Eval coverage
Golden sets with CI regression gates
PII-safe
By default
Redaction, RBAC and audit logging
2 wks
To prototype
A thin slice you can actually judge
- 01Days 1–3
Framed
Job-to-be-done, target metric, constraints and data sources agreed in writing.
- 02Week 1
Prototype
A baseline model or slim RAG slice over your real data—no synthetic demos.
- 03Week 2
Evaluated
Golden sets, failure modes and CI regression gates in place before scaling.
- 04Weeks 3–6
Piloted
Integrated via APIs with cost, latency and quality visible in dashboards.
DELIVERABLES
What We Ship
Swipe to browse
PROCESS
A Calm, Measured AI Process
Swipe through the 4 stages
TECHNOLOGY
Our Stack & Standards
Languages
Python
TypeScript
ML & Deep Learning
PyTorch
TensorFlow
scikit-learn
Hugging Face
OpenAI
Pipelines & Tracking
Airflow
Apache Spark
MLflow
Storage & Search
pgvector
Redis
Elasticsearch
Infrastructure & Cloud
Docker
Kubernetes
AWS
GCP
Azure
Vercel
Tooling
Git
What You Get by Default
Clean datasets: dedupe, PII handling, versioned splits
Grounded responses (RAG) on approved sources where LLMs apply
Guardrails: redaction, filters, fallbacks and audit logs
Latency & cost budgets with caching, streaming and routing
Eval harness: golden sets and regression gates in CI
Docs & runbooks: repos, infra notes and on-call basics
CASE STUDY
From Pilot to Production
CASE STUDY
Knowledge Ops Automation
A document AI pipeline combining extraction and RAG reduced manual review and unlocked trusted answers across thousands of PDFs.
- Sub-second median latency
- Materially lower cost per document
- Evals plus CI regression gates
- PII redaction and audit logs
CASE STUDY
Knowledge Ops Automation
A document AI pipeline combining extraction and RAG reduced manual review and unlocked trusted answers across thousands of PDFs.
Before
- Over 3 minutes average response time
- High cost per document
- Inconsistent quality with no evals
- No audit or PII controls
After
- Sub-second median latency
- Materially lower cost per document
- Evals plus CI regression gates
- PII redaction and audit logs
FAQ
Common Questions
No. We deliver classic ML (classification, regression, forecasting), NLP and computer vision alongside LLM/RAG work. We integrate via APIs, background jobs or event hooks into your systems.
We maintain golden sets, eval pipelines and CI regression gates. For RAG, we re-index content on schedules and enforce guardrails with safe fallbacks.
PII redaction, RBAC, audit logs and retention controls are built in. We can deploy into your cloud and restrict data regions to match policy.
Budgets, caching, streaming and route selection keep spend and response times predictable. We expose all of it in dashboards.
Yes, and we offer a dedicated Chatbots service. This page focuses on broader AI/ML work and operationalisation.
You do. Repos live under your organisation with docs and runbooks for a clean handover.
Start with a thin slice
Ready to turn AI into outcomes?
We’ll identify a high-impact use-case, prove it with a thin slice, then scale with confidence.
- Reply within one business day
- You own the code and assets
- Fixed-scope proposal in 48h
GET STARTED
Tell Us About Your Use-Case
What job should AI do, and which data can we use — records, PDFs, CMS, images? We’ll reply within one business day with a feasibility read.
- 1
We read it properly
A real person reviews your notes and comes back within one business day.
- 2
30-minute scoping call
We pressure-test goals, constraints and timelines. No slide deck.
- 3
Fixed-scope proposal
Scope, milestones and pricing in writing within 48 hours of the call.