Do you only build web-based AI?

No. We deliver classic ML (classification, regression, forecasting), NLP and computer vision in addition to LLM/RAG. We integrate via APIs, background jobs or event hooks into your systems.

How do you keep models accurate over time?

We maintain golden sets, eval pipelines and CI regression gates. For RAG, we re-index content on schedules and enforce guardrails with safe fallbacks.

What about privacy & compliance?

PII redaction, RBAC, audit logs and retention controls are built in. We can deploy into your cloud and restrict data regions to match policy.

How do you manage cost & latency?

Budgets, caching, streaming and route selection keep spend and response times predictable. We expose these in dashboards.

Can you build chatbots?

Yes, and we offer a dedicated Chatbots service. This page focuses on broader AI/ML and operationalisation.

Who owns the code & models?

You do. Repos live under your org with docs and runbooks for a clean handover.

Contact

AI Solutions

Home›Services›AI Solutions

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

Scope Your AI Use-Case See Case Studies

Built for teams that care about reliability over hype

Ops Automation

Risk & Compliance

Docs & Knowledge

Healthcare & Clinics

B2B & SaaS

AI architecture with ingestion, training, RAG and monitoring layers

Prod-Ready

& RAG

USE-CASES

Where AI Pays For Itself

Predictive Models

Classification: lead/churn/risk scoring
Regression: pricing & propensity
Forecasting: demand & staffing (time-series)

Document AI & NLP

RAG over PDFs, sites, Notion & CMS
Extraction, routing, summarisation
Search with semantic & hybrid ranking

Computer Vision

OCR & entity detection
Quality checks on images
Safety & compliance screening

Need a customer-facing assistant? See our Chatbots service.

OUTCOMES

Outcomes You Can Expect

Higher Efficiency

Automate triage, extraction & insights

Predictable Costs

Budgets, caching & routing

Measurable Quality

Evals & regression gates

Compliance Ready

PII safeguards & audit logs

Expect value in weeks: sharper decisions, faster workflows and clear visibility into quality, latency and spend—all surfaced in dashboards.

DELIVERABLES

What We Ship

Included

Custom ML Models

Classification, regression & time-series forecasting tuned to your data.

Included

RAG Pipeline

Ingestion, chunking, embeddings, rerank & freshness for trustworthy answers.

Included

Data Ingestion

Connectors for sites, PDFs, Notion, CMS, DBs + scheduled re-indexing.

Included

Guardrails

PII redaction, content filters, allow/deny lists and safe fallbacks.

Included

Eval Harness

Golden sets, scorecards & CI regression tests for quality you can track.

Included

Integration Hooks

APIs/webhooks to slot models into forms, CRMs and back-office tools.

Included

Cost/Latency Controls

Caching, batching & streaming for predictable performance and spend.

Included

Compliance

Audit logs, RBAC, retention and region controls aligned to policy.

Add-on

Pilot → Rollout

Pilot KPIs, playbooks & channel rollout. (Chatbots available separately.)

PROCESS

A Calm, Measured AI Process

Frame

Define the job-to-be-done, metrics, constraints and data sources.

Use-caseKPIsRisks

Days 1–3

Prototype

Thin slice: data prep + baseline model or slim RAG over your docs.

BaselineRAGGuardrails

Week 1

Evaluate

Golden sets, failure modes and CI regression tests wired in.

EvalsScorecardsCI

Week 2

Pilot & Scale

Integrate via APIs, monitor cost/latency/quality, then rollout.

PilotObservabilityRollout

Weeks 3–6

TECHNOLOGY

Our Stack & Standards

Python

TypeScript

PyTorch

TensorFlow

scikit-learn

Hugging Face

OpenAI

MLflow

Airflow

Apache Spark

pgvector

Redis

Elasticsearch

Docker

Kubernetes

Vercel

AWS

GCP

Azure

Git

Jest

What You Get by Default

Clean datasets: dedupe, PII handling, versioned splits

Grounded responses (RAG) on approved sources when LLMs apply

Guardrails: redaction, filters, fallbacks and audit logs

Latency & cost budgets with caching/streaming/routing

Eval harness: golden sets & 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 (extraction + RAG) reduced manual review and unlocked trusted answers across thousands of PDFs.

Read Full Case Study

Before

> 3 min avg response time
High cost per document
Inconsistent quality (no evals)
No audit/PII controls

After

~ 700ms median latency
-62% cost per doc
Evals + regression gates
PII redaction & audit logs

Case Study

Knowledge Ops Automation

A document AI pipeline (extraction + RAG) reduced manual review and unlocked trusted answers across thousands of PDFs.

Read Full Case Study

Before

> 3 min avg response time
High cost per document
Inconsistent quality (no evals)
No audit/PII controls

After

~ 700ms median latency
-62% cost per doc
Evals + regression gates
PII redaction & audit logs

FAQ

Common Questions

Ready to turn AI into outcomes?

We’ll identify a high-impact use-case, prove it with a thin slice, then scale with confidence.

Start Your Pilot Chatbots Service

Get in Touch

Tell us about your use-case and we’ll reply within 24 hours.