Data infrastructure for the AI era.

AI is only as good as the data underneath it. We design, build and run the platforms investment firms need to put it into production: Snowflake, dbt, semantic layers, Bloomberg and AWS.

Talk to usWhy data first
agent · research-assistant
PM

What was our net exposure to semiconductors at Friday's close?

Net exposure to semiconductors was 11.8% of NAV at Friday's close (Oct 2), down from 12.6% the week before.

Long
18.4%
Short
−6.6%
Net
11.8%

Certified metric · full lineage available

Trace

  1. list_metrics → net_exposure

    "Long minus short market value, as % of fund NAV"

  2. query_metric

    { metric: "net_exposure", by: "gics_industry", as_of: "2026-10-02" }

  3. compiled sql

    select sum(p.long_mv - p.short_mv) / any_value(p.fund_nav)
from analytics.fct_positions_daily p
join analytics.dim_security_pit s
  on s.security_id = p.security_id
 and p.as_of between s.valid_from and s.valid_to
where s.gics_industry = 'Semiconductors'

  4. lineage

    custodian_eod → security_master (point-in-time) → fct_positions_daily

    dbt tests passed · refreshed 06:12 ET

Illustrative example.

The stack we build on

  • Snowflake
  • dbt
  • AWS
  • Bloomberg
  • Terraform
  • Cortex
  • MCP
  • Python
  • Airflow
  • Dagster
  • Iceberg
  • Power BI
  • Tableau
  • Streamlit

Models are a commodity. Your data isn't.

Point an LLM at raw tables and it answers confidently, and wrongly. Give it governed metrics, clear definitions and lineage, and it answers like your best analyst.

That foundation is what we build. Most of an AI project is a data project, and we take care of that part.

  • Metrics defined once, readable by people and models
  • Lineage from every answer back to the source feed
  • Agents with least-privilege access, not the keys to everything

"What's our AUM in Europe?"

  1. Resolved metric aumowner: Finance · certified
  2. Applied region = Europeincludes UK and CH per the firm mapping
  3. Converted to USDWM/Reuters 4pm fix, as of close

Answer

$52.7bn

Certified

Everything between your raw feeds and your AI. One team, every layer.

Ingestion

Every feed, landed and monitored.

Bloomberg, data vendors, custodians, OMS and PMS, APIs, SFTP drops, and the spreadsheets still running the desk.

Platform

Snowflake, set up properly.

Account design, RBAC, warehouses and cost controls. Every agent gets a least-privilege role.

Transformation

Models you can actually trust.

dbt with tests, contracts, CI and lineage. Spreadsheet logic becomes reviewed, versioned code.

Semantic layer

Every number defined once.

AUM, returns and exposures, with definitions that analysts, dashboards and LLMs all read the same way.

AI & serving

AI that queries metrics instead of guessing at tables.

MCP servers, Snowflake Cortex, text-to-SQL and evaluations, alongside the dashboards, notebooks and APIs your teams already use.

Infrastructure

All of it as code.

Terraform-managed AWS and Snowflake, SSO, networking, secrets, CI/CD and monitoring.

Fluent in investment data.

Most data consultancies stop at the warehouse. We work inside investment firms, so we already know why corporate actions break pipelines and why identifiers never quite match.

You spend less time explaining your business, and your AI gets context a generic stack can't give it.

Bloomberg
BLPAPI · B-PIPE · Data License · BQL
Scheduled, monitored and landed in Snowflake, with entitlements respected.
Security master
FIGI · ISIN · CUSIP · SEDOL
Identifier mapping with full history, so every dataset joins on the same instrument.
Corporate actions
Splits · dividends · M&A
Applied consistently across prices, positions and returns.
Point-in-time history
Bitemporal
What you knew and when, with no look-ahead bias in research, backtests or AI answers.
Positions & transactions
Custodian · admin · OMS · PMS
Holdings and trades from every source, reconciled every day.
Performance & risk
NAV · returns · attribution
Calculated once and trusted by front office and operations alike.

From spreadsheets to production AI. One phase at a time, or all the way through.

Research & strategy

We map your sources, workflows and the AI use cases you care about, then design the target architecture and a roadmap your team can act on.

  • AI readiness review
  • Current-state data audit
  • Target architecture
  • Prioritized roadmap

Flexible engagements. Fixed scope, time and materials, monthly retainer or outcome-based. We fit the format and pricing model to your team and your procurement process.

Discuss your project

Trusted with institutional-scale data. Names stay confidential. The diffs speak for themselves.

global-asset-manager.diff

$500B+ AUM

-Excel workbooks and manual Bloomberg exports

-Siloed databases, no single source of truth

+Snowflake platform on Terraform-managed AWS

+Automated Bloomberg and vendor ingestion

+dbt-tested models and a governed semantic layer

Assess Build Run

us-investment-firm.diff

$20B AUM

-No central data platform

-Spreadsheets reconciled by hand

+Data strategy and cloud platform built from scratch

+Automated ingestion and dbt transformations

+Self-serve analytics

Assess Build Run

Let's talk about your data.

Tell us where you are today and what you would like AI to do with your data. An engineer, not a salesperson, will get back to you.

Prefer email?

contact@aperiogen.com