AI capability

AI that survives contact with the work.

Most AI projects fail for the same reason: they automate a process nobody had defined. We start with the repetitive judgement work already in front of you, automate what is safe to automate, and keep a person in the loop wherever a decision is expensive to undo.

Scope an AI pilot

Straight answer first

When AI is worth it, and when it is not.

Most AI projects fail because they automate a process nobody had defined. This is the test we apply before quoting anything.

When AI is worth it

Repetitive judgement work, enough historical data to learn from, a clear definition of a good outcome, and someone who can review the output where a decision is costly to reverse. Those four together are the signal.

When it is not

An undefined process. AI on top of an unclear workflow produces confident nonsense faster than a human produces careful nonsense. Fix the process first — it is cheaper and it is often the whole answer.

What we do about it

Discovery starts with the workflow, not the model. If the honest answer is that you need better data capture rather than a model, we say so and quote that instead.

How we prove it

A narrow pilot against real data with a measurable baseline, before any platform commitment. If it does not beat the baseline you stop, having spent weeks rather than quarters.

AI capability

AI services we actually ship.

Not pilots that stall at the demo. Twelve ways we put AI into the systems your teams already use, each with a person in the loop wherever a decision carries weight.

Healthcare AI

AI diagnosis assistance, medical document analysis, clinical decision support, patient-risk prediction, healthcare chatbots

Computer Vision AI

Object detection, face recognition, OCR, medical imaging, surveillance, quality inspection

Generative AI

AI chatbots, AI copilots, content generation, document generation, custom GPT-style applications

Conversational AI

AI voice agents, customer-support bots, WhatsApp/website assistants, appointment booking agents

Predictive AI & Machine Learning

Forecasting, recommendation engines, churn prediction, demand prediction

NLP & Document AI

Invoice processing, contract analysis, document extraction, summarization, semantic search

AI Automation

AI-powered workflows, lead qualification, customer onboarding, email automation, business-process automation

Recommendation & Personalization AI

Product recommendations, personalized feeds, content recommendations, user-behavior analysis

AI Agents & Autonomous Systems

Multi-step AI agents, research agents, sales agents, coding agents, enterprise copilots, agentic workflows

Fraud Detection & Risk Intelligence

Transaction fraud scoring, anomaly detection, identity and KYC checks, credit-risk models, AML alert triage

AI Strategy & Roadmap

AI opportunity assessment, use-case prioritization, costed roadmaps, build-versus-buy analysis, governance and compliance

MLOps & AI Platform

Versioned data and models, drift monitoring, retraining pipelines, shadow and A/B deploys, rollback on release
Talk to AI Expert Learn More

Start with a pilot, not a platform.

Four to eight weeks against real data with a measurable baseline. If it does not beat the baseline you stop, having spent weeks rather than quarters.

Scope a pilot

How we work

How an AI engagement runs.

The same six steps as any other build. What differs is that step four measures a model against a baseline rather than a spec.

  1. Discovery

    A written scope and a costed roadmap, from one structured conversation about what "done" looks like.

  2. Design

    Journeys, wireframes and a clickable prototype — so layout arguments cost an afternoon, not a sprint.

  3. Build

    Two-week sprints against the agreed scope, with a working demo at the end of each.

  4. Test

    Functional, performance and security testing, plus user acceptance on real data.

  5. Launch

    Staged rollout with monitoring and a rollback path, planned before go-live.

  6. Support

    Ninety days included: fixes, patching and performance. Then a retainer or a clean handover.

Technology

The stack we build on.

Model choice is a constraint, not a preference: frontier APIs where quality matters most and data can leave your environment, open weights self-hosted where it cannot. The architecture stays model-agnostic so switching later is configuration, not a rebuild.

  • Models & serving

    • Claude
    • GPT
    • Llama
    • Mistral
    • vLLM
    • Ollama

  • Retrieval & data

    • pgvector
    • Qdrant
    • Elasticsearch
    • LangChain
    • LlamaIndex

  • Evaluation & ops

    • Eval suites
    • LangSmith
    • Prompt versioning
    • Guardrails
    • Audit logging

Client reviews

What it is like to work with us.

Founders and directors who have shipped with us, in their own words.

  • Tang Renhong, China Executive Director, Simcare

    “Exceptional service from start to finish. The team was highly professional, responsive, and dedicated to meeting our needs.”

  • Peter, Israel Founder and CTO, Herd-itt

    “A truly outstanding experience! Their innovative solutions helped our business grow and improve efficiency significantly.”

  • Dr Sam Swapn, California Founder, Strategism

    “Reliable, efficient, and always exceeding expectations. Their expertise transformed our approach and enhanced our results.”

  • Dr Sanjay Jain Director, Akhil Systems

    “Exceptional service from start to finish. The team was highly professional, responsive, and dedicated to meeting our needs.”

  • Dr Shyatto Rahha Founder, MyHealthcare

    “Exceptional service from start to finish. The team was highly professional, responsive, and dedicated to meeting our needs.”

  • Nivesh Khandelwal & Vishwas Singh CEO, Nivaan

    “Exceptional service from start to finish. The team was highly professional, responsive, and dedicated to meeting our needs.”

Questions, answered

AI questions, answered straight.

Including the ones where the honest answer is that you do not need AI.

Four things together are the signal: the work is repetitive judgement rather than mechanical, you have enough historical data to learn from, there is a clear definition of a good outcome, and a person can review the output where a decision is expensive to undo. Miss any one and the honest recommendation is usually to fix the process first. We assess that in discovery and will tell you when the answer is no.

Not with the architectures we build. We use enterprise API tiers with training disabled, or self-hosted open-weight models where the data cannot leave your environment at all. Which of those applies is a decision we make with you in the first week, because it constrains everything after it.

Retrieval against your own content with citations, so an answer can be checked rather than trusted, plus evaluation sets that run on every change and approval gates at any step that is expensive to undo. Hallucination is not eliminated by prompting; it is contained by architecture.

A scoped pilot against real data is typically $12,000 to $30,000 and runs four to eight weeks. Production systems depend on integration surface more than on the model. We would rather sell you the pilot first, because it tells both of us whether the production build is worth commissioning.

Whichever fits the constraint. Frontier APIs where quality matters most and data can leave your environment; open-weight models self-hosted where it cannot, or where per-token cost at your volume makes APIs uneconomic. The architecture is kept model-agnostic so switching later is a configuration change, not a rebuild.

Yes — multi-step agents that read, decide and act in your systems, with approval gates wherever a step is expensive to undo. The engineering that matters is not the prompting; it is the tool boundaries, the retry and rollback behaviour, and the audit trail of what the agent did and why.

From the team

Notes on building software.

What we have learned shipping products, written up for the people who have to make the same calls.

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