Solutions for Enterprise
AI is not a feature we add. It is how we work.
Every InWork engagement — from a marketing campaign to a six-figure SaaS platform — starts with AI design. Our AI-first methodology is not a sales talking point; it is our internal operating model, applied to your transformation.
What AI-first actually means
Built natively, not bolted on.
Most companies that say "AI-first" mean they use ChatGPT to write emails. That is not AI-first. Here is what it means at InWork.
AI opportunity first
Every project begins with an AI opportunity assessment — before the spec is written, we ask where AI creates leverage in the system.
AI architecture before code
We design the AI architecture first — which models, which orchestration layer, which data structures, which human-in-the-loop touchpoints — before code architecture.
Native, not a plugin
The data model, API structure, and workflow design all account for AI from the start, so the system compounds with every model improvement.
We use AI ourselves
Our developers, marketers, and QA team use AI in their own work daily. We are not selling AI transformation while ignoring it internally.
The 90-day AI transformation sprint
Discover, build, deploy — in 90 days.
The standard delivery model for an enterprise AI transformation engagement.
Days 1–30 — Discover & Design: current-state assessment of workflows, data, tools, and team readiness; AI opportunity mapping by business function; architecture design for the 2–3 highest-value initiatives; and governance and compliance planning.
Days 31–60 — Build & Pilot: MVP development of 1–2 AI systems, integration with existing CRM/ERP/marketing stack, and a pilot with your internal team or selected customers, with feedback and iteration.
Days 61–90 — Deploy & Measure: production deployment, team enablement and documentation, KPI measurement and reporting, and a roadmap for the next phase.
At 90 days you have 1–2 production AI systems live, measured productivity or efficiency impact, a 12-month AI strategy document, and a team trained to operate in AI-augmented workflows.
The InWork AI Engine™
A four-layer architecture, multi-model and cost-routed.
Our internal delivery framework governs how every AI system we build is architected — with known performance characteristics, cost controls, and fallback behavior.
Application layer
Document intelligence, AI chatbots and voice agents, risk scoring and predictive models, content generation, and process automation.
Orchestration layer
LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, and Haystack — for tool-use agents, RAG, and multi-agent coordination.
Model layer
OpenAI, Anthropic Claude, Google Gemini, Meta LLaMA, Mistral, and Azure OpenAI — with multi-model routing that assigns each task to the most cost-effective model meeting the quality threshold.
Data & embedding layer
Pinecone, pgvector, Weaviate, ChromaDB, and Redis Vector, with hybrid BM25 + vector retrieval and reranking for maximum recall.
Infrastructure & delivery
Enterprise-grade infrastructure with US oversight.
What stays constant when you engage InWork at enterprise scale — the quality doesn't change, the economics do.
60–70%
Reduction in LLM API costs via multi-model routing
Not every task requires a frontier model. Our routing logic assigns each task to the most cost-effective model that meets the quality threshold — cutting LLM API costs 60–70% versus routing everything through a single top-tier model.