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AI Automation & Agents· Web Application

Narratize

Primary AI development partner for Narratize's earliest generative features: document ingestion, technical briefs, research reports and scientific analysis.

  • Sector Product innovation, R&D
  • Client US AI platform for new product innovation
  • Role Primary AI development partner
Pass rate on the content-writing evaluation, up from 60%
88%
Every change scored automatically on GitHub Actions
CI evals
OpenAI, Anthropic and Mistral in one platform
Multi-model
Speak an idea, hear a narrated story back
Voice agent
Narratize AI platform: document ingestion, technical briefs and AI-assisted innovation workflows
The starting point

The challenge

Product innovation teams spent weeks turning research, internal documents and scattered ideas into technical briefs and decision-ready reports. Narratize needed generative features that experts would trust: grounded in their own sources, consistent in structure and fast enough to use every day.

The solution

What we built

  • Worked as the platform's primary AI development partner, turning the product vision into working AI capabilities for its earliest features.

  • Built document ingestion that brings research papers, internal files and notes into a structured knowledge layer.

  • Designed generation workflows for technical briefs, research reports and scientific analyses, combining OpenAI, Anthropic and Mistral models.

  • Connected research sources such as PubMed and arXiv, so drafts can draw on published work.

  • Added a voice storytelling agent: users speak, and hear a narrated, AI-generated story back, using Deepgram speech-to-text and text-to-speech.

Architecture

How the system works

How it fits together
  1. 01SourcesDocuments, notes, PubMed, arXiv
  2. 02IngestParse and structure knowledge
  3. 03GenerateBriefs, reports, analyses
  4. 04Model layerOpenAI, Anthropic, Mistral
  5. 05EvaluateCI suites on GitHub Actions
  6. 06DeliverDrafts in the product, or by voice
Engineering

How it was engineered

  • Ran evaluation suites in CI on GitHub Actions, so every prompt or pipeline change was scored automatically. This was before LangSmith was mature enough to rely on.

  • Built the evaluation sets from curated examples of briefs and reports, and kept them in the repository alongside the code.

  • Supported releases across time zones, including late-night fixes when the client team needed them.

Scope

What was delivered

01

Document ingestion pipeline

02

Brief, report and analysis generation workflows

03

Multi-model orchestration

04

Research source connectors

05

Voice storytelling agent

06

CI evaluation suites

Technology

Stack

PythonDjangoStreamlitOpenAIAnthropicMistralDALL·EDeepgramLangChainGitHub Actions
Client feedback

In the client’s words

“I value your skill and dedication to your craft… I've worked with a lot of engineers over my career and have seen all different types, and I'm happy we found a group like yours which is of the highest quality.”
Engineer on the client teamNarratize · via Slack
“[Our co-founder] was a thoughtful and collaborative partner who took the time to understand what we were trying to build and the problems we wanted to solve. [He] helped translate our product vision into working AI capabilities, contributing to the development of some of Narratize's earliest product features.”
Former executiveNarratize · via LinkedIn
“I want to send a huge thank you to everyone who worked on making this feature possible, through all the troubleshooting and problem solving. It's so exciting to finally have this live and in front of users!”
Product teamNarratize · via Slack

Need something similar?

We can scope and build a system with comparable capabilities, shaped around your own workflows, data and integrations.