Web Development
Server-rendered applications, headless commerce and enterprise portals built for sub-second loads.
Explore
Intelligent Systems
Applied AI, Autonomous Agents & Workflow Automation
Most AI projects fail for unglamorous reasons: the data was never prepared, nobody defined what correct looks like, and the pilot had no route into production. We work backwards from a measurable business outcome, establish an evaluation set before writing prompts, and treat retrieval quality as an engineering discipline rather than a demo trick.
Ecosystem matrix
The stack we build on
Mainstream, well-supported technology chosen so your team can hire for it. We will justify any choice on this list, and we avoid exotic tools that create a dependency on us.
Python
LangChain
LlamaIndex
OpenAI API
Gemini API
Pinecone
pgvector
PyTorch
FastAPI
n8n
Make
Hugging Face
Deliverable blueprint
Enterprise data sandboxing, vector embeddings, high-precision task execution.
Sub-services deep dive
5 specialist capabilities
Expand any capability for its core scope, architectural blueprint and typical delivery window.
Language models adapted to your domain vocabulary and quality bar, with an evaluation suite that tells you objectively whether a change made things better.
Core capabilities
- Model selection benchmarked on your data, not public leaderboards
- Supervised fine-tuning and preference optimisation where it pays
- Automated evaluation harnesses with regression gates
- Output guardrails, grounding checks and escalation paths
Architectural blueprint
Curated training corpus β fine-tune or adapter layer β evaluation harness gating release β versioned inference endpoint with fallback.
Delivery timeline
6β12 weeks
Fine-tuning, evaluation harnesses, guardrails
Assistants that answer from your documentation, ticket history and product data β and hand off to a human when confidence drops, rather than inventing an answer.
Core capabilities
- Retrieval pipelines with chunking and reranking tuned to your corpus
- Tool-calling agents that take real actions in your systems
- Multilingual support including Arabic and Urdu
- Confidence thresholds with human handoff and full transcript audit
Architectural blueprint
Document ingestion β embedding store (Pinecone/pgvector) β hybrid retrieval with reranking β grounded generation with citation enforcement.
Delivery timeline
5β10 weeks
RAG, multilingual, tool-calling agents
The unglamorous, highest-ROI work: eliminating the recurring manual handoffs between systems that quietly consume entire salaries.
Core capabilities
- Process mining to find where hours are actually going
- Durable, resumable workflows with retry and compensation logic
- Document intake, classification and system-of-record routing
- Exception queues so humans handle only genuine edge cases
Architectural blueprint
Trigger sources β Temporal or n8n orchestration β idempotent task workers β exception queue with human review interface.
Delivery timeline
3β8 weeks per process
n8n, Make, Python, Temporal
Forecasting and scoring models tied to a decision someone will actually make, with honest confidence intervals rather than a single flattering number.
Core capabilities
- Demand forecasting, churn prediction and lead propensity scoring
- Feature pipelines with drift detection and scheduled retraining
- Explainability outputs so decisions can be defended
- Dashboards that surface the recommended action, not just the chart
Architectural blueprint
Warehouse feature store β training pipeline with holdout validation β registered model β batch and real-time scoring endpoints.
Delivery timeline
6β12 weeks
Forecasting, churn, propensity modelling
Turning unstructured paper, scans and camera feeds into validated structured data your systems can act on without a human retyping it.
Core capabilities
- Invoice, contract and identity document extraction with validation
- Arabic and mixed-script recognition tuned for regional documents
- Visual inspection and defect detection on production lines
- Confidence scoring with targeted human-in-the-loop review
Architectural blueprint
Capture β preprocessing and deskew β OCR/vision inference β schema validation β confidence-routed review queue β downstream system.
Delivery timeline
6β14 weeks
OCR, IDP, defect detection, Arabic script
How we engineer
A four-stage accelerated lifecycle
The same sequence on every engagement, whether it runs six weeks or two years. Each stage has an exit condition, so nobody discovers a disagreement in month four.
STAGE 01
Discover
We map the business outcome, constraints and existing estate before proposing anything. Discovery ends with a costed plan, not a sales deck.
1β3 weeks
STAGE 02
Architect
Rendering strategy, data model, integration contracts and the non-functional requirements are settled and documented as decision records.
2β4 weeks
STAGE 03
Execute
Fortnightly increments against a visible backlog, with working software demonstrated every cycle rather than status reported.
6β24 weeks
STAGE 04
Scale
Load testing, observability, runbooks and knowledge transfer β then either you take it in-house or we operate it under an SLA.
Ongoing
Featured case study
Regional insurance group
Before → After
Claims intake used to be twelve people reading PDFs. Now it is two people handling exceptions.
— Head of Claims Operations, Regional insurance group
Claim processing time — before
3.4 days
after
19 min
Extraction accuracy — before
β
after
98.6%
Manual review rate — before
100%
after
7%
Annual cost saved — before
β
after
$1.9M
Engagement tiers
Indicative pricing
Published because opaque pricing wastes everyone's time. Figures are in USD and indicative; a fixed quotation follows discovery.
Discovery & Pilot
$34,000
Fixed scope Β· 6 weeks
A working pilot on real data with an evaluation harness that proves whether it is worth scaling.
- Use-case prioritisation workshop
- Data readiness assessment
- Working pilot on production data
- Evaluation set and accuracy baseline
- Go/no-go recommendation with costs
Production Deployment
$98,000
Most selected Β· 14 weeks
The pilot hardened, integrated and monitored β running against live business volume.
- Everything in Discovery & Pilot
- Production integration with your systems
- Guardrails and human-in-the-loop routing
- Monitoring, drift detection and alerting
- Team training and runbooks
- 90 days tuning support
Intelligence Platform
Custom
Multi-quarter programme
A shared AI foundation several teams build on, with governance to match.
- Everything in Production Deployment
- Shared retrieval and inference platform
- Model governance and audit framework
- Self-hosted deployment option
- Dedicated ML engineering squad
- Quarterly model review and retraining
Answers
Frequently asked technical questions
No. We architect for data isolation using enterprise API tiers with zero-retention agreements, or self-hosted open-weight models where policy demands it. The data boundary is documented and agreed before any integration work starts.
Retrieval grounding with citation enforcement, so responses must point at a source document. We add confidence thresholds that trigger human handoff, and we test against an adversarial evaluation set built specifically to provoke fabrication.
Process automation typically returns its cost within four to seven months, and we quantify the baseline before starting so the claim is verifiable. Generative AI projects vary far more widely, which is why we scope a paid discovery phase rather than promising a number upfront.
Not for most deployments. We hand over monitoring dashboards, retraining runbooks and alerting on drift. For continuously learning systems we recommend either a retainer or a named internal owner with a few days of training from us.
Yes β Snowflake, BigQuery, Databricks and standard Postgres warehouses are all routine. We read from your warehouse rather than duplicating it, which keeps governance in one place.
Frequently paired with