Datics AI
← All work

Insurance Sales Lab

Insurance Sales Lab ships platform features with our engineers in its own repository

Insurance Sales Lab trains U.S. insurance sales agents on its own platform. Our engineers build its features in its repository and stack, directed through its daily syncs, and that work led ISL to hire us for two AI products.

EdTechInsurance sales agency platformForward deployed engineering

Model
Datics engineers working in ISL's own repository
Rhythm
Daily syncs, with ISL setting direction
Shipped
Call tracking, dashboards, CSV import, team admin
What came next
Two AI products for the same client

The challenge

Insurance Sales Lab needed engineers to keep adding features to its own sales-agency platform, built its way, in its code and under its direction.

What we did

We built call tracking integrations, daily statistics and sales team dashboards, CSV import and team management directly in ISL's repository, through its daily syncs.

The outcome

The platform keeps gaining features from inside ISL's own code, and ISL went on to hire us for an AI voice roleplay trainer and then a desktop call recorder.

The starting point

Insurance Sales Lab is a U.S. company that trains insurance sales agents. Its sales-agency platform is a custom CMS in React and Node.js with GraphQL, TypeScript on both ends and PostgreSQL, kept in ISL's own repository.

What had to change

ISL needed new features on that platform, from call tracking to team dashboards. It wanted engineers who would build them in its code and to its priorities, not a separate product beside it.

What could not break

  • Features ship into ISL's own repository, not a codebase of ours
  • The existing React, Node.js, GraphQL and PostgreSQL stack stays as it is
  • ISL sets priorities in its daily syncs

The calls we made

We treated the platform as ISL's product and our engineers as part of how it gets built: its repository, its stack, its daily syncs.

  1. 01

    Build in ISL's repository

    Every feature went straight into ISL's own code on its existing stack, so ISL owns and understands all of it.

    Ruled out

    Building features in our own codebase and handing them over.

  2. 02

    Direction through daily syncs

    ISL set priorities in daily syncs, so engineering always worked on what ISL decided mattered next.

    Ruled out

    A fixed scope built out of sight.

  3. 03

    One point of coordination

    Our team lead coordinated with ISL, so ISL had one place to raise priorities and questions.

    Ruled out

    ISL managing each engineer separately.

  4. 04

    Let the scope grow with the product

    The contract described general feature work; we took on what the platform needed next, from integrations to team management.

    Ruled out

    Holding the work to the first description.

How the system fits together

prioritiesprogresswhat to buildnextbuilt in itsrepositorystorescall dataISL teamSets priorities anddirectionDatics engineersBuild features, oneteam leadDaily syncISL's direction eachdayNew platformfeaturesDashboards, CSVimport, teams,…ISL sales-agencyplatformReact, Node.js,GraphQL, TypeScriptPostgreSQLPlatform dataCall trackingprovidersConnected throughintegrations
PeopleBuilt by DaticsClient's existing systemData storeOutside serviceISL sets direction in daily syncs, and every feature lands in its own platform code and database.

What we built

01

Call tracking integrations

Call tracking integrations built into the platform on its existing stack.

02

Daily statistics and team dashboards

Daily statistics and sales team dashboards, added inside ISL's existing platform.

03

CSV import and team management

CSV import into the platform, plus organization and team management.

What changed

ISL's platform kept gaining features from inside its own code. The trust built there led ISL to hire us for an AI voice roleplay trainer and later a desktop call recorder.

Under the hood

Datics role
Forward deployed engineering
Codebase
ISL's own repository: React and Node.js with GraphQL, TypeScript on front and back end, and PostgreSQL.
Integrations
Call tracking providers connected to the platform, and CSV import for bringing data in.
Process
Daily syncs with ISL setting direction, and one team lead coordinating with ISL.

Data and retrieval

PostgreSQL

Apps and APIs

ReactNode.js

Also in the stack

TypeScriptGraphQL

How it shipped

  1. Start on ISL's platform

    We began with feature work on ISL's custom CMS, learning its code and stack in its own repository.

  2. Features, iteration by iteration

    Call tracking, dashboards, CSV import and team management shipped into the platform, steered through daily syncs.

  3. Then the AI products

    ISL hired us for an AI voice roleplay trainer, and later a desktop call recorder, each as its own project.

  4. Live and ongoing

    The platform is live and in use, and the work continues inside ISL's repository.

In ISL's repository

Our engineers built directly in ISL's code on its existing stack, so the platform stays one codebase ISL owns.

Daily syncs

ISL directed the work in daily syncs, and our team lead was its single point of coordination.

What this unlocked

The platform work led straight to two AI products for ISL: an AI voice roleplay trainer and a desktop call recorder, each with its own case study.

Related work

Have a product with the same kind of constraints?

Tell us what already exists and what you want it to do. We will tell you what it takes.

Book a 30-minute Product Review