Task agents
Scoped agents that act — re-engagement outreach, ticket triage, stock anomaly checks.
Pillar I · Transform · 02
AI that ships into production — and stays there.
Most AI engagements stall between a proof-of-concept and a system nobody will run. We close that gap. Agents, copilots, document intelligence and predictive models — governed, monitored, and operated by the same team that built them.
Earlyto working prototype
Model-agnosticAnthropic · OpenAI · Azure · Bedrock
PDPL-readyresidency by default
AI pattern library
Four ways AI actually lands — in production.
Operational copilots
Embedded in CRM, service desk, planning. Cut cycle time on human-in-the-loop work.
Document intelligence
Contracts, invoices, clinical notes, engineering drawings — extracted and routed.
Predictive & decision
Demand, maintenance, churn, anomaly. Classical ML where it fits — we don't force LLMs.
Prototype → production pipeline
The path every model takes, before it goes near a user.
01Data
Your sources: CRM, ERP, ticketing, files. Access-controlled and governed.
›02Grounding
Retrieval, context, policy guardrails. Zero PII where it doesn't need to leave.
›03Model
Anthropic, OpenAI, Azure OpenAI, Bedrock or on-prem — model-agnostic by design.
›04Evaluate
Offline + online evaluations on your data. Regression gates before release.
›05Deploy
Behind your identity, audit log, rate-limit and human-in-the-loop controls.
›06Monitor
Drift, quality, cost and safety — measured continuously, alerted on breach.
Governance & safety
What ships with every agent.
Not a policy doc — concrete, enforced defaults applied from the first prototype.
Data residency and PDPL alignment by default
Policy guardrails on prompts and outputs
Audit logs and traceable decision paths
Evaluation harness and drift monitoring
Per-role access, per-user rate limits
Kill-switches and rollbacks on every agent
Practical questions
The things we get asked.
Can you work with our existing LLM and cloud stack?+
Yes. We build against the model provider and cloud you already use — Azure OpenAI, Anthropic, Google, AWS Bedrock, open-weights models on your own infrastructure. Model-agnostic is a design goal, not a bolt-on.
How do you handle data residency and PDPL?+
We architect for in-Kingdom data residency and PDPL alignment by default — keeping personal and sensitive data inside your compliance envelope, with clear routing rules for what can and cannot leave.
Do you only do generative AI?+
No. Classical ML, forecasting and optimization are often the right answer. We pick the model that fits the problem — including the boring, well-proven one.
Have a use case in mind? Let's scope a quick prototype.
Most AI conversations start with: "we've run POCs, nothing shipped." Tell us where they stopped — we'll pick up from there.
Talk to our AI team