The flow is simple and utilises the full power of ipushpull's capital markets expertise. The process involves several stages: Ingest, where daily transcript files are securely received as raw chat files; Identify & Link, which detects quote actions, normalises speakers, and groups updates into message-level rows; Extract & Enrich, where instrument, price, size, side, and owner are pulled, and reference data and metadata are applied to enrich the data, resulting in normalised quote events; and finally, Publish, where the data is pushed to blotter, API, warehouse, or Excel, providing both live and historical feeds.

This isn’t just about cleaning up chat. It’s about pulling commercial value out of previously wasted data. The outcomes include new data opportunities, such as creating richer quote and IOI feeds for clients; efficiency at the desk, by starting the day with a pre-populated blotter of client interests; knowledge retention, ensuring insight remains institutional rather than desk-bound or siloed; analytics and reporting, surfacing customer trends, behaviours, and position changes; and historical intelligence, enabling the building of years of machine-readable data.

Turning Forgotten Chat into Tomorrow’s Data

by Andrew Capewell • 29 June 2025 • 3 min read

In a previous blog, we looked at the Capture layer. In this blog, we will review ipushpull's orchestration capability and how it effectively maps and brings structure to your chat data. Every day, across countless chat windows, brokers and traders talk about pricing, probe interest and test the waters. Then the day ends, the chats are archived and all that messy, valuable data drifts into history.

What if it didn’t?

ipushpull can take those end-of-day chat message transcripts and extract something actionable: structured quote data, complete with timestamps, context, and source messages. No more lost quotes. No more lost intent. Just a clean view of what happened, when and enabling you to act on it.

This is a textbook example of our Orchestrate pillar in motion.

Capture the raw, unstructured chat.
Orchestrate it into structured, contextualised output.
Deliver it wherever it’s needed: Quote Hub, OMS, Data-as-a-Service or Excel.

How It Works:

The flow is simple and utilises the full power of ipushpull's capital markets expertise:

Stage	Purpose	Output
Ingest	Securely receive daily transcript files	Raw chat file
Identify & Link	Detect quote actions, normalise speakers, and group updates	Message-level rows
Extract & Enrich	Pull instrument, price, size, side and owner. Apply reference data & metadata to enrich the data	Normalised quote events
Publish	Push to blotter, API, warehouse, Excel	Live and historical feed

Some of the key capabilities that make this work:

Handles conversational quotes. Conversations aren’t clean. This keeps multiple, mixed quote threads sorted and intact.
Context-aware enrichment. When someone says “same as before”, the system remembers what that means.
Nested blotter view. See every add, update, or cancel as expandable rows with full traceability.
Validated data. This is a ready-made data product for downstream analytics, partners, or internal teams.

Why It Matters:

This isn’t just about cleaning up chat. It’s about pulling commercial value out of previously wasted data.

Outcome	Benefit
New Data Opportunities	Create richer quote and IOI feeds for clients
Efficiency at the Desk	Start the day with a pre-populated blotter of client interests
Knowledge Retention	Keep insight institutional, not desk-bound or siloed
Analytics and Reporting	Surface customer trends, behaviours, and position changes
Historical Intelligence	Build years of machine-readable quote history, ready for flow analysis
Audit Trail	Store the original chat line alongside every derived quote for full transparency

This is where ipushpull’s deep market knowledge and software expertise really come together. We’re not guessing how these conversations work. We’ve lived them. And we’re building tools that make sense for how brokers and traders actually operate.

Where It’s Headed

The platform is already running and ready to demo.

We’re treating this as a foundation for broader quote capture and order orchestration tools. You’re sitting on a mountain of unstructured chat; this is a simple way to turn it into value.

Try It Out

Share a transcript.
We’ll process it and deliver a blotter and flat file.
You tell us what’s right, what’s missing, and we improve.
If it works, we agree terms and start sending a daily feed.

This is one of several use cases under ipushpull’s Orchestrate pillar. Others, like trade capture, booking and quote enrichment, follow the same model, structured, auditable, context-rich data flowing into the systems and people that need it.

That’s how you build real automation in capital markets. And it starts with listening to the data you already have. Got more questions? Why not contact us to discuss further.

Frequently asked questions

How can I turn archived trader chat into structured quote data?

ipushpull ingests daily chat transcript files and extracts structured quote events from them. It detects quote actions, normalises speakers and pulls out instrument, price, size, side and owner, then applies reference data. The output can go to Quote Hub, an OMS, Data as a Service or Excel.

What happens when a quote in chat is updated or cancelled?

ipushpull groups updates to the same quote and shows them in a nested blotter view. Every add, update or cancel appears as an expandable row with full traceability to the source message.

What can a trading desk do with historical chat quote data?

The post lists richer quote and IOI feeds for clients and a pre-populated blotter of client interests at the start of each day. It also supports analytics on customer trends and position changes. Over time it builds years of machine-readable quote history for flow analysis.

How do I trial ipushpull's chat transcript processing?

Share a transcript and ipushpull processes it into a blotter and a flat file. You say what is right and what is missing, and ipushpull improves the output. If it works, both sides agree terms and a daily feed begins.

Learn more about Chat Data Mining from ipushpull.

Overview

Capture

Orchestrate

Deliver

Data Services Connectors

Client Integrations