Usha International runs on dozens of systems that were never designed to talk to one another. Until that context is unified, every AI initiative quietly re-solves the same integration problem — and value stalls in proofs of concept.
Our view: the durable advantage is a single enterprise context layer that carries the business from visibility, to explanation, to autonomous execution.
Captured from 28 sources across strategy, leadership, lines of business, competition, geographies, capabilities and recent signals — and used to ground everything below.
The maturity curve runs from visibility, to explanation, to natural-language reasoning, to action — and each step depends on the one before it.
We integrate data across silos — not systems, but business concepts — into a single 360° view.
Integrate & build 360We model the relationships between entities and stakeholders so a dip can be traced to its cause.
Model relationshipsA conversational layer over semantic models turns plain-language questions into grounded answers.
Converse over contextAutonomous agents act on the same context — raise the order, reassign, requisition, resolve.
Act autonomouslyBuilt bottom-up, because trust compounds upward: agents are only as safe as the copilot's grounding, the copilot only as reliable as the graph, the graph only as complete as the 360° model beneath it.
Instead of integrating systems, we integrate business concepts into one unified model — regardless of which system at Usha International they live in.
Enterprise 360 creates the entities; the graph creates the relationships, so AI can traverse cause to effect.
LLMs sit on top of the graph and a semantic layer, so every answer is grounded — a trusted answer, not a confident hallucination.
Agents share the same graph and semantic layer — they read the context, predict, and execute a real transaction in the source system.
Enterprise 360 is the foundation every layer above reuses. We don't integrate systems — we integrate business concepts. Usha International's data stays where it is; SCIKIQ connects, contextualizes and resolves it into trusted entity 360s.
200+ no-code connectors pull from every source — no rip-and-replace, no data movement.
The metadata & contextualization engine maps each field to a shared business glossary.
Entity resolution stitches records across systems into one record per real-world entity.
Lineage, quality & access control attach to every entity — so the 360 is trusted.
One trusted profile per customer — across every channel and system.
Every asset with its live health, history and failure risk.
Orders, service levels and capacity in one operational view.
Every supplier with SLA, spend and delivery risk.
Revenue, cost and exposure traceable to their operational drivers.
A context layer is years of integration, governance and graph engineering. We bring the platform — you bring the domain.
Dashboards answer what. Only a graph + semantic layer answers why — and lets agents act.
A fabric moves data. SCIKIQ adds the business context every AI initiative needs to be trusted.
Ungrounded models hallucinate. Grounded on the graph, answers are explainable and lineage-traced.
When the four layers are in place, leadership sees a single live view — every number traceable to its source, every alert to its root cause.
Entities and their typed relationships as one connected, physics-driven graph. Drag nodes, scroll to zoom, click to inspect — or trace the live scenario from root cause to business outcome.
Ask in plain language — grounded on the live operational graph. Language models supply the fluency; the graph supplies the truth.
Every agent draws on the same graph and semantic layer — then closes the loop with a real transaction in the source system.
“Can we trust it?” — answered by design, not by promise.
Every leader sees themselves — each use case builds on a business 360 and the four pillars it needs: 1 Enterprise 360 · 2 Knowledge Graph · 3 AI Copilot · 4 Agent Factory.
Trace revenue impact to its operational cause; auto-resolve invoice exceptions.
See congestion and asset risk before they bite; reassign capacity autonomously.
Every number lineage-traced; policy-enforced answers for the board and regulators.
Unify the customer across systems; act on churn and service signals in real time.
Vendor SLA, inventory and requisitions — predicted and closed-loop.
A single, live, trusted view of the business — from the top floor.
Built once, the context layer becomes shared infrastructure — so each new AI initiative starts from enterprise context, not a blank integration backlog.
We would prove the context layer in three focused sprints — earning the right to scale with evidence, not slideware.
Harvest & connect
Model the entities
Answer the hard questions
Position this not as a Data Fabric, but as “the enterprise context layer for AI and agentic automation at Usha International.” On comparable engagements: 30–50% faster analytics delivery, 20–40% less integration effort, and a foundation every future AI initiative builds on rather than rebuilds.