Direction 01
AI-accelerated tracker pipelines
Your data scientists build s from , , and models. AI helps them draft and run those pipelines faster, grounded in ODP methods, with a human sign-off on every .
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Spin up a new company KPI tracker
Each new means wiring , , your , , and . Most of that work follows patterns your team already uses, but it still takes days of manual .
Solution: A agent indexed on ODP's reads the scientist's intent, retrieves similar trackers and internal formulas, drafts the full , and opens a . The scientist reviews, edits, and signs off before anything ships to .
Tools: , , , ,
How it looks in practice
1 · Scientist intent
"Build an orders tracker for RetailCo from UK receipt panel + web scrape. Match our retail methodology."
2 · Codebase blocks retrieved
odp/normalize/receipt_panel.py
Reused from ApparelCo tracker
odp/models/orders_v3.py
Proprietary blend formula
odp/backtest/point_in_time.py
House validation harness
3 · Pipeline scaffold generated
Scrape
UK receipt + web
Clean
ODP normalize blocks
Model
Multi-signal blend
Backtest
Out-of-sample check
Also generated
- · Unit tests for ingestion + normalization
- · Methodology note for audit trail
- · Backtest report vs last 8 quarters
Nothing ships to production until a data scientist reviews, edits, and approves the merge.
Keep alternative data feeds running
, web , and break when a site or changes. A silent gap can pollute before anyone notices.
Solution: Always-on monitoring detects breaks. An agent diagnoses the issue using ODP's patterns, proposes a fix as a , and pings the owner. The scientist approves the . No more finding broken feeds by accident downstream.
Tools: , , ,
How it looks in practice
1 · Break detected
Scraper stopped · 6 hours ago
Vendor portal HTML changed. Selector .txn-row no longer matches. Zero rows ingested since 02:00 UTC.
2 · Impact on estimates
3 retail trackers depend on this feed. Daily orders estimate for RetailCo would gap without intervention.
3 · Agent diagnosis + fix
Matched ODP scraper pattern · retail_receipt_v2
Before
.txn-row
After
.transaction-item
Scientist approves merge. Feed resumes before the next daily estimate run.
Effect: Scientists spend less time on and more on . New trackers move from intent to reviewable draft in hours, not days, without sacrificing .
Direction 02
Data quality & research automation
ODP ingests a huge volume of every day. AI watches those feeds, flags what looks wrong, and drafts research output your analysts can review before it reaches clients.
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AI data quality & anomaly detection
Dozens of update daily across 250+ companies. When a source goes , KPIs , or a signal looks wrong, scientists cannot check every feed by hand.
Solution: AI automatically checks incoming feeds and flags broken sources, missing data, strange movements, or possible . The outcome is alerts, priority tickets, and short explanations for analysts: what changed, why it looks suspicious, and which company/ needs review.
Tools: , , , ,
How it looks in practice
Flagged for review
RetailCo · orders
P1Volume down 68% vs 7-day baseline
What changed: UK receipt panel stale. Last row: Tue 02:14 UTC.
Suggested action: Check scraper + confirm before publish
TravelCo · bookings
P2KPI moved +22% with no sector corroboration
What changed: Single vendor spike. Possible false signal.
Suggested action: Compare against flight + app panels
Delivered to analyst
Plain-language summary
"RetailCo orders look wrong because the receipt feed stopped updating. Do not publish today's estimate until the feed is restored or manually validated."
Problems surface at ingestion, not after a client sees a bad number.
AI research copilot for investor reports
ODP publishes daily revenue and estimates for 250+ plus deep . Analysts spend hours turning moves into client-ready commentary for and .
Solution: AI reviews daily changes across companies and sectors, then drafts short investment-style comments, chart summaries, client-ready , and Q&A notes for calls. The analyst edits and approves before anything goes out.
Tools: , , ,
How it looks in practice
1 · KPI moves scanned
US Retail sector
Broad softness in mid-tier apparel
2 · Draft client outputs
Investment comment
Receipt panel softness in mid-tier apparel. Watch RetailCo and BrandCo into earnings. Talking point: channel shift vs macro headwind.
Also drafted
- · Chart summary for sector deck
- · 3 talking points for hedge fund call
- · Q&A: "Why did AppCo MAU diverge?"
Effect: Data problems surface before they hit . Analysts start from a draft, not a blank page, and ship client-ready research faster.
Case studies
Real automations we have shipped. Each one follows the same shape: the AI does the heavy lifting, and a person stays in control of the decisions that matter.
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AI document and data intake
Messy documents in, clean data out, a person makes the final call.
The situation
A US real estate fund manages more than $500 million. Every week it gets a flood of offers to buy new buildings. The offers arrive as PDFs, spreadsheets, and slide decks, and every broker formats them differently. Only about 1 in 100 offers turns into a deal, but someone still has to read all of them. That reading used to be done by hand, by expensive analysts.
How it works
Pull in the offer
The system watches the inbox and pulls in every attachment: PDFs, Excel models, and slide decks.
How it looks in practice
1240 Market St offer
Broker · Kessler
Riverside portfolio · 4 assets
Seller · Nova RE
Oak Business Park
Broker · Hale
Automation
- · Watches broker inbox 24/7
- · Pulls PDF, Excel, and deck attachments
- · Queues each offer for extraction
- · No analyst copies files by hand
Read it and fill the model
AI reads the documents, pulls out the key numbers, and fills the fund's own model. Then it works out , , risk, and fit.
How it looks in practice
Extracted fields
Projected ROI
11.2%
Portfolio fit
7.5 / 10
Fund model populated automatically
AI reads the broker pack, maps numbers into the fund's own template, and runs yield, ROI, and risk calculations.
Summarize for a person
It writes a short summary with the highlights, the pros and cons, and the risks. A manager reads it and makes the call.
How it looks in practice
AI summary
Recommended next step
Worth a deeper look. Request rent roll and confirm lease rollover before scheduling a site visit.
Human makes the investment call. AI only prepares the first review.
What it delivers (illustrative)
- A first review drops from about 30 to 60 minutes down to about 3 to 5 minutes.
- The team can handle 5 to 10 times more offers without hiring more people.
- Senior staff stop copying numbers between PDF and Excel, so each review costs less.
- The fund replies to brokers faster, which is a real edge in a fast market.
A sales agent that feels human
Talks to leads by email, consults them, and sends the proposal for signature.
The situation
A company sells beverage services for events and parties. Sales reps used to spend their days answering the same email questions and writing proposals by hand. We built an AI sales agent that handles the whole conversation. People do not realize they are talking to an AI.
How it works
Read and reply to leads
The agent reads each incoming email, works out what the client wants, and replies in a natural, human tone.
How it looks in practice
events@vinerie.co
“Need a drinks package for a 200-person launch in June. Budget around $2k, mostly mocktails.”
Agent action
Replies in a natural tone within minutes. Asks 2 clarifying questions about venue and timing. Client does not know it is AI.
Consult and bring in help
It answers questions and recommends the right packages. When a request needs a person, it quietly loops in a teammate.
How it looks in practice
Client: what can we do under $2k, no alcohol?
Agent: recommends 3 mocktail bar packages with staffing, glassware, and timing for a 200-guest launch.
Client: can you include a bartender and setup by 5pm?
Packages compared
- · Classic mocktail bar · $1,450
- · Premium botanical bar · $1,780
- · Self-serve station · $980
Send the proposal to sign
When the deal looks good, it builds a proposal in and sends the commercial offer for on its own.
How it looks in practice
Summer Launch Beverages
Agent builds and sends the proposal without a sales rep touching the document.
What it delivers (illustrative)
- Leads get a helpful reply in minutes, any time of day.
- The agent writes and sends proposals without a rep touching them.
- People step in only for the tricky conversations.
- The team handles far more leads without growing .