01 / Practice
Agentic AI systems
Agents that finish work, not just answer questions
Proven in AgentOS
An agent is only useful if it can plan, pick the right tool, recover when a
step fails, and stop when it should. That is an orchestration and boundary
problem far more than a prompting one — which is why we built the platform
layer ourselves before offering it as a service.
Multi-agent orchestration
Planning, tool use and multi-step execution, with the memory and permission boundaries that keep autonomy predictable.
Retrieval that fits your estate
Vector, hybrid or fully vectorless — database-agnostic by design, so retrieval works on the datastore you already run, even air-gapped.
Conversational & voice agents
Dialogue systems, question answering and speech in and out, grounded in your own content rather than the open web.
Evaluation & guardrails
Golden corpora, adversarial critics and refusal behaviour, so quality is measured continuously rather than assumed.
- LiteLLM
- FastAPI
- RAG
- Knowledge graphs
- Whisper
- Eval harnesses
Fine-tuning is worth doing when it buys accuracy on your vocabulary, lower
latency, lower cost per call, or the ability to run on your own hardware.
We will tell you when prompting or retrieval would get you there first —
that answer is cheaper for you and more honest from us.
Dataset curation
Labelling strategy, synthetic augmentation and a held-out set built before training starts, not after.
Adaptation
LoRA and PEFT methods for domain, tone and task, sized to the smallest model that meets the bar.
Measured iteration
Every run scored against the same evaluation set, so an improvement is demonstrated rather than felt.
Serving & on-device
Quantisation, inference optimisation and deployment — including on-device, as in Haramayn Guide.
- PyTorch
- LoRA / PEFT
- Quantisation
- scikit-learn
- Model serving
A model that is right 92% of the time still needs an interface that handles
the other 8% gracefully. We design for latency, uncertainty and trust:
streaming output, visible sources, sensible degradation, and an honest
"I don't know" instead of a confident invention.
Conversational interfaces
Chat, voice and assistive surfaces that stream, stay interruptible, and show their working.
Offline-first design
Local storage, on-device inference and sync strategies for places the network cannot be relied on.
Explainability surfaces
Citations, provenance and confidence shown in the product, not buried in a log.
Human-in-the-loop
Review and approval flows for decisions a business is not willing to fully automate — and should not.
- Next.js
- React
- PWA
- Capacitor
- IndexedDB
- Streaming APIs
04 / Foundation
The engineering underneath
Platforms, pipelines, automation and deployment
Proven in Agrozent
AI is a component inside a system that also has to authenticate users, move
data, survive traffic, keep customer information private and be deployable on
a Tuesday afternoon. We build that part too — it is usually where projects
actually stall, and it is where production-grade is won or lost.
Platform & API development
REST and service architectures, authentication, integration with the systems you already run.
Automation & data pipelines
Python automation, document and PDF processing, resilient scraping with change detection and alerting.
Data & storage
PostgreSQL, Supabase, Redis and search indexes, with migrations treated as append-only history.
Security, privacy & hardening
Encryption at rest, tenant isolation, role-based access, audit trails, rate limiting and abuse defence — deployed self-hosted, on-premise or air-gapped when your data cannot leave.
- Python
- FastAPI
- Flask
- TypeScript
- PostgreSQL
- Redis
- Docker
- Nginx
- CI/CD