Transform Your Business with AI
Generative AI, chatbots, RAG assistants, agents, automation, document intelligence and predictive analytics — built so they hold up outside the demo. Grounded in your data, measured before launch, and monitored for cost after it.
- AI service lines
- 16AI service lines
- Grounded in your data
- AlwaysGrounded in your data
- Evaluated before launch
- YesEvaluated before launch
- Provider lock-in
- NoneProvider lock-in
We start by asking whether AI is the right tool
A surprising number of problems described as AI problems are solved better by a rule, a database query or a redesigned form — faster, cheaper and far more predictable. Every AI engagement begins with a build-or-skip assessment, and we will tell you plainly when the answer is skip.
When AI genuinely is the right tool, the engineering around it decides whether it survives real users: grounding, evaluation, guardrails, fallbacks and cost control. That is where most of our work goes.
Sixteen ways we apply AI to business problems
Each service line describes what it does and the engineering that makes it dependable — not just the capability.
Generative AI Applications
Applications where generation is the product — drafting, summarising, rewriting, extracting and translating at a scale humans cannot match.
- Model selection matched to task and budget
- Prompt templates versioned like code
- Output evaluation before release
AI Chatbots
Assistants that answer from your actual content and hand over cleanly to a person when they should.
- Grounded in your documents and policies
- Escalation to human agents with context
- Web, WhatsApp and in-app channels
RAG Applications
Retrieval-augmented generation that answers from your knowledge base with citations, so answers can be checked.
- Chunking and embedding strategy tuned to your content
- Hybrid keyword and semantic retrieval
- Citations on every answer
AI Knowledge Assistants
Internal assistants that let staff query policies, SOPs, contracts and historical records in plain language.
- Permission-aware retrieval per user role
- Connectors to drives, wikis and databases
- Usage analytics on what people actually ask
AI Automation
Judgement-dependent steps — classification, routing, extraction, triage — automated where accuracy can be measured.
- Confidence thresholds with human fallback
- Accuracy measured against a labelled set
- Full decision logging for audit
AI Agents
Task-completing agents that call your tools and APIs to finish work, not just describe how it would be done.
- Typed tool definitions with validation
- Step limits, timeouts and cost ceilings
- Approval gates before irreversible actions
Agentic AI Systems
Multi-agent systems that plan, delegate, use memory and recover from failure across long-running workflows.
- Planner, worker and reviewer separation
- Shared memory and state management
- Observability across every agent step
LLM Applications
Production LLM features with the engineering that makes them reliable: caching, retries, streaming and cost control.
- Streaming responses and graceful degradation
- Semantic caching to cut cost and latency
- Provider-agnostic abstraction layer
AI-Powered Search
Search that understands intent, handles synonyms and ranks by meaning rather than exact keyword overlap.
- Hybrid semantic and lexical ranking
- Faceting, filtering and personalisation
- Relevance tuning against real queries
Document Intelligence
Extraction and validation of structured data from invoices, forms, contracts and scanned documents.
- OCR for scanned and photographed documents
- Schema-validated structured extraction
- Review queue for low-confidence fields
AI Workflow Automation
End-to-end business processes where AI handles the interpretation and deterministic code handles the actions.
- AI for understanding, code for execution
- Deterministic, replayable side effects
- Exception queues with clear reasons
Recommendation Systems
Personalised product, content and next-action recommendations tuned to a metric you actually care about.
- Collaborative and content-based hybrids
- Cold-start handling for new items and users
- A/B testing built into the rollout
Predictive Analytics
Forecasting demand, churn, delay and risk from your historical data, delivered where decisions are made.
- Feature engineering from your own systems
- Backtested accuracy before deployment
- Predictions surfaced inside existing tools
Machine Learning Applications
Classical ML where it outperforms an LLM — cheaper, faster, more predictable and far easier to explain.
- Classification, regression and clustering
- Explainability for regulated decisions
- Scheduled retraining on fresh data
AI-Enabled SaaS
Multi-tenant products with AI features, including per-tenant data isolation, usage metering and margin control.
- Strict per-tenant retrieval isolation
- Usage metering and plan limits
- Unit-economics dashboard per feature
Custom AI Integrations
AI capability added to the systems you already run — ERP, CRM, support desk or internal tools.
- Integration with existing databases and APIs
- No rip-and-replace of working systems
- Incremental rollout, feature by feature
How an AI engagement actually runs
The difference between an AI prototype and an AI product is almost entirely in these six things.
Assess the use case
We start by checking whether AI is the right tool. If a rule, a query or a form would solve it better, we will say so — before you spend anything.
Ground it in your data
Useful systems answer from your documents, records and policies. We design the retrieval and permission model before choosing a model provider.
Measure before launch
We build an evaluation set from real examples and score every change against it, so quality is a number rather than an impression.
Guard the edges
Guardrails, confidence thresholds, human review paths and hard limits on irreversible actions — designed in, not added after an incident.
Control the cost
Token usage, latency and spend are monitored per feature, with caching and model routing to keep unit economics sane at scale.
Keep it portable
A provider-agnostic layer means you can switch or mix models as prices and capabilities change, without rewriting the product.
The AI stack, and what each part is for
The intelligence layer. We use these where they measurably beat a rule or a query — and we tell you when they do not.
Language understanding and generation for assistants, extraction, summarisation and classification.
Producing drafts, summaries, replies and structured output at volumes manual work cannot reach.
Grounding answers in your own documents with citations, so responses can be verified rather than trusted blindly.
Systems that call your tools and APIs to complete a task, with limits and approvals around risky actions.
Multi-step, multi-agent workflows with planning, memory and recovery for long-running processes.
Prediction, classification and clustering on your own data — often cheaper and more explainable than an LLM.
Turning raw operational data into the features, insight and dashboards decisions are made from.
Versioning, deploying, monitoring and retraining models so they keep working as the world changes.
Embedding AI decisions into business workflows with confidence thresholds and human fallback.
AI systems we have built
Case studies describing the problem, the engineering approach and the outcome — without invented statistics.
AI
Conversational AI
AI Chatbot
Customer support
- Business problem
- A large share of support volume consisted of the same handful of questions, but a generic chatbot had previously damaged trust by confidently inventing answers that were wrong.
- Result
- Routine questions are resolved without an agent while genuinely novel ones reach a person quickly with the conversation attached. Gaps in documentation surface as data instead of as complaints.
Key features
- Answers grounded in approved company content
- Clean escalation to human agents with full context
- Web, in-app and WhatsApp channels from one core
- Python
- FastAPI
- LLM APIs
- PostgreSQL
- Next.js
- +1 more
AI
Retrieval-augmented AI
RAG Knowledge Assistant
Knowledge-intensive services
- Business problem
- Staff spent significant time searching policy documents, contracts and historical records spread across several systems — and answers varied depending on who was asked.
- Result
- Staff get cited answers in plain language, scoped to what they are allowed to see. Because retrieval quality is scored against a maintained evaluation set, changes can be shipped without guessing whether they helped.
Key features
- Hybrid semantic and keyword retrieval
- Citations linking every claim to a source passage
- Permission-aware retrieval scoped to user role
- Python
- FastAPI
- Vector database
- LLM APIs
- PostgreSQL
- +1 more
AI
Agentic AI
AI Agent Platform
Operations automation
- Business problem
- Multi-step operational processes required a person to move information between systems and apply judgement at each hop. Simple scripted automation broke on the exceptions, which were common.
- Result
- Routine paths complete without human involvement while exceptions surface with a full trace of what the system tried. Because every run is observable, failures are diagnosable rather than mysterious.
Key features
- Typed tool definitions with schema validation
- Planner, worker and reviewer agent separation
- Step limits, timeouts and per-run cost ceilings
- Python
- FastAPI
- LLM APIs
- Redis
- PostgreSQL
- +1 more
The questions businesses actually ask
Including the uncomfortable ones about cost, accuracy and lock-in.
Sometimes it is not, and we will say so. If a rule, a database query or a better form solves the problem, that is cheaper, faster and more predictable. We start every AI engagement with a build-or-skip assessment.
By grounding responses in your own documents and data, requiring citations, and enforcing confidence thresholds so the system says it does not know rather than guessing. Quality is measured against an evaluation set before release, not assessed by impression.
It depends on usage and model choice. We instrument token usage and spend per feature from day one and apply caching and model routing to keep unit economics viable. You get a cost projection before committing.
We configure providers to exclude your data from training, and for sensitive workloads we can use private or self-hosted models. Data handling terms are agreed in writing before any integration.
No. We build behind a provider-agnostic layer so models can be switched or mixed as prices and capabilities change, without rewriting the application.
Describe the problem, not the technology
Tell us what is slow, expensive or error-prone in your business today. We will tell you whether AI helps, what it would take, and what it would cost to run.
Build an AI Solution
Grounded in your data. Measured before launch. Monitored for cost. Portable across providers.