The AI Engineering Report Q3 2026 is now available! Read the latest research:
THE SPEED TRAP
Platform
Platform
Overview

Monitor and measure what your teams + AI produce, optimize model routes and agent context for max efficienciy, and govern adoption AI across teams

Capabilities
Observe

Track token spend, outcomes, and AI engineering efficiency across your teams

Optimize

Model routing and context engineering that cut token waste, maximize outcomes

Govern

Comprehensive policies and guardrails to minimize AI adoption drisk

Solutions
Solutions
AI ROI

Maximize AI returns across development teams, tools, and outcomes

AI Spend Visibility

Track AI coding costs and uncover what drives your spend

AI Token Allocation

Allocate AI spend to team budgets and forecast what’s next

AI Governance

Enforce model, budget, and data policies across developers and agents

Developer Productivity

Understand AI's impact on delivery, quality, and productivity

Optimal Model Routing

Route tasks using model performance proven on your own code

Build Your Own Bench

Turn shipped work into benchmarks for smarter AI coding decisions

Cover of the Faros AI Engineering Report Q3 2026, The Speed Trap

AI Engineering Report Q3 2026: The Speed Trap is now available

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Engineering in the AI era

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Reports on AI engineering

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See who ships faster

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Cover of the Faros AI Engineering Report Q3 2026, The Speed Trap

AI Engineering Report Q3 2026: The Speed Trap is now available

Read the Takeaways
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Cover of the Faros AI Engineering Report Q3 2026, The Speed Trap

AI Engineering Report 2026: The Speed Trap is now available

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Leah McGuire

Leah McGuire

Leah McGuire has spent the last two decades working on information representation, processing, and modeling. She started her career as a computational neuroscientist studying sensory integration and then transitioned into data science and engineering. Leah worked on developing AutoML for Salesforce Einstein and contributed to open-sourcing some of the foundational pieces of the Einstein modeling products. Throughout her career, she has focused on making it easier to learn from datasets that are expensive to generate and collect. This focus has influenced her work across many fields, including professional networking, sales and service, biotech, and engineering observability. At Faros, she develops the platform’s native AI capabilities.

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Latest posts by Leah McGuire

Research
20
min
3/13/25

Does Copilot Improve Code Quality? The Cause and Effect Data Is In

Does GitHub Copilot improve code quality? Our causal analysis reveals its true impact on PR size, code coverage, and code smells.
Cause and effect relationship with Copilot logo
AI
Productivity
Platform Engineering
Leaders
Coding
20
MIN READ
AI Industry
18
min
11/8/24

Mastering Domain-Specific Language Output: Getting an LLM to Perform Reliably without Fine-tuning

See how real-world user insights drove the latest evolution of Faros AI’s Chat-Based Query Helper—now delivering responses 5x more accurate and impactful than leading models.
AI
Data Engineering
18
MIN READ
AI Industry
15
min
2/12/24

Lessons from Implementing LLMs Responsibly at Faros AI

How we used GenAI to make querying unfamiliar data easier without letting the LLM take the wheel
A banner image of Leah McGuire, machine learning engineer at Faros AI, with the article title "Lessons from implementing LLMs responsibly at Faros AI."
AI
Data Engineering
15
MIN READ
Faros
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