industries · 08

Capital Markets. Six workloads, one endpoint.

Broking, exchanges, clearing & institutional — the workloads below need more than one data model at once, which is exactly why they stall on a stitched stack.

who this is sized for

Retail and institutional brokers, exchanges and clearing corporations, investment banks and proprietary trading firms operating under SEBI's 2026 framework.

who owns the problem
Chief Technology OfficerHead of SurveillanceHead of ResearchChief Risk OfficerHead of Algo & ExecutionChief Compliance Officer
why now

The forcing function.

From April 2026, SEBI requires an exchange-assigned ID on each algorithmic order, with full lifecycle logging auditable on demand — and the consolidated Stock Brokers Regulations 2026 replaced the 1992 rulebook. Retail and F&O volumes have exploded, and SEBI is itself scaling AI to catch manipulation. Brokers need graph-based detection of their own — over the same store that holds tick data, client records and research embeddings.

the relationship graph

The surveillance graph — spoofing, layering and circular trading are coordinated shapes across accounts and fund flows.

April 2026

exchange-assigned Algo-ID and full order-lifecycle auditability mandated by SEBI

CSCRF + DPDP

cyber-resilience and residency obligations demanding local control and full trails

5 → 1

time-series, vector, graph, search and relational — five licences become one

the shape of the work

Six workloads, five query shapes.

Almost no workload here needs only one data model — which is why single-model databases deliver it only with a second system and a sync problem.

data-model mix across these six workloads
SQL 6/6
Vector 4/6
Graph 2/6
Full-text 2/6
/ask 2/6
workload × data model
SQL
VEC
GRF
FTS
ASK
01 Market surveillance & manipulation detection — uses Graph, Vector, SQL
02 Algo audit & order traceability — uses SQL, Full-text, /ask
03 AI research & advisory copilots — uses Vector, Full-text, SQL
04 Client 360, suitability & cross-sell — uses SQL, Graph, Vector
05 Real-time risk & margin monitoring — uses SQL, /ask
06 Execution & transaction-cost analytics — uses SQL, Vector
SQL — what is true right now? Vector — what resembles this? Graph — what is this connected to? Full-text — where exactly is it written? /ask — just tell me, in plain language.
where OriginChain powers Capital Markets AI
01
Market surveillance & manipulation detection

Model accounts, orders and fund flows as a live graph to surface spoofing, layering, circular trading and front-running rings invisible to siloed rule engines.

Graph Vector SQL

Coordinated patterns detected before the exchange raises them — a finding, not a penalty.

02
Algo audit & order traceability

Full order-lifecycle logging keyed to exchange Algo-IDs, spanning OMS, risk and execution, answerable in plain language on demand.

SQL Full-text /ask

Regulator-grade evidence produced on request rather than reconstructed under deadline.

03
AI research & advisory copilots

Multilingual retrieval across research reports, filings and earnings calls, joined live with market data and the client's actual holdings.

Vector Full-text SQL

Research leverage per analyst; advisory scaled without proportional headcount.

04
Client 360, suitability & cross-sell

KYC, holdings, risk profile and interactions unified — graph relationships plus semantic search drive next-best-action and retention.

SQL Graph Vector

Higher revenue per active client and measurably lower attrition.

05
Real-time risk & margin monitoring

Positions, exposure and pre-trade limits in one store — kill-switch triggers and margin breaches queryable instantly, with no BI backlog.

SQL /ask

Intraday risk visibility; fewer surprise breaches at settlement.

06
Execution & transaction-cost analytics

Post-trade best-execution, slippage and venue analytics — pattern search over historical fills joined with live and reference data for sharper routing.

SQL Vector

Basis points recovered on execution quality, compounding across volume.

Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.

the consolidation case

The stack this replaces in Capital Markets.

OMS Tick store Surveillance rules Research drive Warehouse
OCDB — one substrate
SQL Vector Graph Full-text /ask

Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.

the board-level outcome

Surveillance, research and risk on one auditable substrate — built for the 2026 rulebook rather than retrofitted to it.

Start with your data challenge — not a product demo.