Innovative Solutions

Innovative Solutions Rebuilds Enterprise Services Delivery with Fireworks AI

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Innovative Solutions Innovative Solutions Rebuilds Enterprise Services Delivery with Fireworks AI

PUBLISHED 5/5/2026

Table of Contents

Executive Summary

Scaling services delivery with Agent Systems

The Problem: Services Velocity and AI Inference Economics Were Hitting Structural Limits

The Decision Moment: Choosing an Inference Layer That Wouldn’t Slow Them Down

The Economic Inflection Point

The Solution: A Multi-Agent Execution System Across the Services Lifecycle

The Impact: From Linear Services to Parallel Execution

Why Fireworks

Looking Ahead: The Rising Future of Multi-Agent Economics

Closing

Table of Contents

Executive Summary

Innovative Solutions, a Tier 1 AWS Premier Partner delivering hundreds of AI-driven services engagements annually, hit a structural scaling constraint as inference costs and delivery complexity increased together. AI inference became the dominant cost driver in the business, limiting margin expansion and operational flexibility at scale. To address this, the company moved its DarcyIQ platform to Fireworks AI as its primary inference layer. This reduced model integration overhead, stabilized multi-model execution, and made costs predictable. This was not a tooling change. It was a redesign of services economics around AI systems. The result was a shift from linear delivery models to parallel, agent-driven execution across sales, scoping, and delivery. Results

• Contract cycles reduced from 30–45 days to ~3 days • Delivery throughput doubled across engineering and PM teams • AI inference shifted from linear cost growth to predictable, controllable economics • Multi-agent execution scaled to 4–10B tokens per month, doubling month over month

Scaling services delivery with Agent Systems

Innovative Solutions is an AWS Premier Tier Partner helping enterprises and mid-market teams design and deploy AI systems at scale. As engagement volume increased, the team built DarcyIQ to streamline how proposals, technical documentation, and delivery artifacts were generated. What began as an internal productivity tool evolved into a core execution layer for services delivery, later expanding into a commercial platform used by agencies, GSIs, and ISVs. Today, DarcyIQ sits at the center of how the company delivers AI-enabled services. CTO Travis Rehl has led the shift toward agentic delivery systems designed to increase throughput without proportional headcount growth. The Problem: Services Velocity and AI Inference Economics Were Hitting Structural Limits

As the business scaled, two issues emerged simultaneously. Delivery bottleneck

Consultants and engineers managed multiple concurrent engagements, creating constant context switching across customers, tools, and models. Coordination overhead increased faster than capacity, limiting throughput even as demand grew. Cost structure pressure

As Travis put it, “Our number one COGS is AI cost. Our costs were keeping up with our acquisitions”. Contracting and scoping cycles typically took 30–45 days from first meeting to signed agreement, slowing revenue realization and delaying delivery start. With the business doubling month over month, inference spend scaled directly with usage, eliminating operating leverage. At scale, this meant growth no longer translated into margin expansion. The Constraints

Three constraints defined the problem: 1. Model iteration was slow and operationally heavy

Every model change required engineering effort, validation, and deployment coordination, especially when working across rapidly evolving frontier models like GLM-5 and Kimi K2.5. 2. Costs scaled linearly with usage

Inference costs increased directly with usage, preventing margin expansion at scale. 3. Delivery execution was saturated with repeatable tasks

Significant engineering time was spent on scoping, documentation, and proposal generation instead of differentiated delivery. As the company moved toward multi-agent workflows, inference density increased and cost predictability became critical. At this point, scaling required architectural change, not optimization. The Decision Moment: Choosing an Inference Layer That Wouldn’t Slow Them Down

As Innovative Solutions evaluated inference providers including Baseten, the core requirement wasn’t just performance or cost. It was operational: They needed a system that could handle constant model changes without slowing teams down in operation. As they rotated between models like GLM-5 and Kimi K2.5, every change introduced validation work, engineering overhead, and deployment delays. Fireworks removed that friction. As Travis described, “Fireworks won simply because it worked consistently. Whenever we deploy any model, it works the first time. No tuning, no fiddling. That mattered to us, because we change models all the time. What I don’t want is to get stuck in a 3-week development cycle trying to make a model work.” In a system where models are constantly changing, consistency at deployment becomes a scaling constraint. That moment clarified the decision. Stability and zero-friction deployment weren’t nice-to-haves. They were requirements for scaling a multi-agent system in production. Within 1-2 weeks of initial deployment, 90% of Anthropic inference spend had been migrated to Fireworks , making it the default inference layer for DarcyIQ. The Economic Inflection Point

Once Fireworks was in place, the scaling behavior changed. Instead of costs rising directly with usage, inference became predictable even as workloads expanded: • 4–10B tokens per month • Doubling month over month • Increasingly multi-agent workloads

This shifted DarcyIQ from a constrained system into a production-grade execution. The Solution: A Multi-Agent Execution System Across the Services Lifecycle

With Fireworks as the inference layer, DarcyIQ evolved from a productivity tool into a multi-agent execution system that operates across the full services lifecycle, from first customer interaction to delivery. These capabilities depended on high-performance, stable inference that could support real-time generation, rapid model iteration, and sustained multi-agent workloads at scale. 1. Real-Time Contract & Scope Generation

Customer conversations are converted directly into structured scopes, proposals, and...