Solutions

Reference builds and accelerators we deploy and adapt — built on patterns we've proven in production.

SOL·01

Enterprise RAG knowledge platform

Governed ingestion, chunking and embedding, vector store, citation-backed answers over your own knowledge base — not a wrapper around a search box.

TYPICAL TIMELINE
4–6 weeks to a working pilot on one knowledge domain.
BASED ON
the retrieval and grounding patterns built for Vodafone's enterprise GenAI platform.
FIG·01 — ENTERPRISE RAG FLOW INGEST GOVERNED SOURCES EMBED CHUNK + VECTORIZE STORE VECTOR DB ANSWER CITED, TRACEABLE
SOL·02

Agentic news / document intelligence pipeline

Autonomous ingestion and summarisation of high-volume documents or feeds, with a critic agent that rejects any claim it can't trace to a source.

TYPICAL TIMELINE
6–8 weeks for a first pipeline in production.
BASED ON
the agentic news-pipeline architecture designed for Urekaa.ai, our stock-analysis product currently in pre-launch.
FIG·02 — AGENT + CRITIC LOOP INPUT WORKFLOW AGENT ADK / LANGGRAPH CRITIC SOURCE CHECK OUTPUT GROUNDED ANSWER
SOL·03

PII-safe document processing for GenAI

Encrypted intake, DLP-based redaction, key management that keeps plaintext out of the job graph — so sensitive documents are safe to put in front of a model.

TYPICAL TIMELINE
4–6 weeks to a governed pipeline for one document type.
BASED ON
the secure document pipeline built for Vodafone's enterprise GenAI programme.
FIG·03 — PII-SAFE DOCUMENT FLOW INTAKE PGP ENCRYPTED VAULT KEY MANAGEMENT DLP PII REDACTION OUTPUT GOVERNED + AUDITABLE
SOL·04

Cloud data platform migration factory

Estate assessment, a repeatable migration framework, and migration waves run in parallel with verification — so the hundredth server costs a runbook, not a project.

TYPICAL TIMELINE
2–3 weeks assessment, then waves of 4–8 weeks each.
BASED ON
the framework built for Vodafone's 600+ server, 11-country Neuron migration.
FIG·04 — MIGRATION WAVE SEQUENCE ASSESS ESTATE INVENTORY WAVE 1 PARALLEL RUN WAVE 2 VERIFY + CUTOVER WAVE N REPEAT
SOL·05

Continuous ML retraining loop

Prediction logging, drift monitoring, scheduled and drift-triggered retraining, evaluation-gated promotion — models that improve after launch instead of decaying.

TYPICAL TIMELINE
6–8 weeks to the loop running end-to-end on one model.
BASED ON
the Capability-1 specification for continuous retraining — the same architecture Urekaa.ai's retraining loop will run on at launch, currently in pre-launch.
FIG·05 — THE RETRAINING LOOP LOG PREDICTIONS + OUTCOMES TRAIN CANDIDATE MODEL EVAL GATE BEAT THE CHAMPION PROMOTE CANARY ROLLOUT