Agentic systems, retrieval and decision science, engineered for production.
We build AI systems as software systems: typed interfaces, evaluation gates, observability and explicit cost models, integrated with the data platforms that feed them and the core systems they act upon.
Discuss an AI programme-
We engineer agentic systems that plan, invoke tools and execute multi-step workflows across ERP, CRM, ticketing and document estates, governed by a deterministic control plane that enforces typed tool contracts, least-privilege scopes, budget ceilings and escalation paths to human approvers.
Architectures pair LLM planners with durable workflow engines and event-driven state machines, so every autonomous action is replayable, auditable and reversible. Process mining establishes the automation baseline; production evaluation tracks task-completion rate, human-intervention rate and cost per resolved case.
- Multi-agent orchestration
- Tool calling & MCP
- Durable workflow engines
- RPA augmentation
- Human-in-the-loop approvals
- Agent evaluation
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Retrieval-augmented generation engineered as an information-retrieval problem first: layout-aware parsing and chunking, hybrid lexical and dense retrieval, cross-encoder re-ranking and knowledge-graph augmentation, with generation constrained to cite verifiable sources.
We deploy copilots over policy, legal, clinical, sales and service knowledge with PII redaction, prompt and response filtering, and access-control-aware retrieval that propagates document-level entitlements. Groundedness, answer relevance and hallucination rate are measured offline and in production.
- Hybrid search
- Cross-encoder re-ranking
- GraphRAG
- Vector databases
- Guardrails
- LLM evaluation
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Lakehouse and warehouse architectures with medallion layering, change-data-capture ingestion, streaming pipelines and data contracts between producers and consumers, so analytics, ML features and AI retrieval draw on a single governed source of truth.
Semantic layers and metric stores give leadership consistent KPIs, while lineage, quality monitors and attribute-based access policies keep the estate auditable. FinOps controls keep storage and compute proportional to the value they create.
- Lakehouse
- CDC & streaming
- Data contracts
- Semantic layer
- Quality & lineage
- Embedded analytics
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Supervised, probabilistic and time-series models for demand forecasting, credit and fraud risk, churn, propensity, dynamic pricing and inventory optimisation, framed against a business decision and an economic objective rather than an accuracy metric in isolation.
Feature stores, experiment tracking, model registries and CI/CD for ML move models from notebook to monitored service. Drift, bias and performance monitors trigger retraining pipelines under documented model-risk controls.
- Time-series forecasting
- Gradient-boosted models
- Causal inference
- Mathematical optimisation
- Feature store
- MLOps
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Layout-aware OCR, vision-language models and schema-constrained extraction convert invoices, claims, KYC packs, contracts, medical records and handwritten forms into validated structured data, with field-level confidence scoring and exception routing to reviewers.
Computer-vision pipelines cover ANPR, object detection, visual inspection and occupancy analytics at the edge or in the cloud, with quantised models for constrained hardware.
- Intelligent document processing
- Vision-language models
- Table & form extraction
- ANPR
- Edge inference
- Human review queues
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Voice and chat agents that resolve rather than deflect, integrated with core systems for authentication, order, claim and appointment workflows across the WhatsApp Business Platform, web, mobile and contact-centre telephony.
Speech pipelines combine streaming ASR, LLM dialogue management and neural TTS, with code-mixed Indic language support and Arabic across Modern Standard and Gulf dialects, measured on containment, first-contact resolution and CSAT.
- WhatsApp Business Platform
- Voice agents & IVR
- Streaming ASR / TTS
- Indic & Arabic NLP
- Contact-centre integration
- Conversation analytics
