Enterprise Solution

Transition AI Pilots into Production Systems

Closing the last-mile AI gap. We deploy Forward Deployed AI Engineers directly into your engineering team to harden prototypes, eliminate hallucination vulnerabilities, optimize inference latency, and achieve true enterprise ROI.

Deploy Production Pod Explore AI Stack
Delivery Focus Production Hardened
Sprint Timeline 4–8 Weeks
Inference Profiling p95 Latency Tuning
Telemetry Ingestion 100% Traced

The "Last Mile" AI Engineering Dilemma

Most modern enterprises have successfully built an internal AI demo or proof of concept. Yet engineering surveys indicate that the majority of enterprise AI initiatives struggle to cross into reliable, production-grade deployment.

The breakdown occurs because building a prototype in a notebook or isolated sandbox is fundamentally different from operating an AI system under production constraints:

  • Data Pipeline Drift: Production documents and databases are unstructured, constantly mutating, and fragmented across disparate legacy systems.
  • Non-Deterministic Failures: Unconstrained LLM outputs lead to schema parsing failures, broken JSON contracts with downstream APIs, and hallucinated references.
  • Latency & Cost Inflation: Multi-second response times and unoptimized token usage create cost explosions at enterprise query volumes.
  • Security & Access Controls: RAG retrieval models that bypass user-level role-based access control (RBAC) create critical data privacy vulnerabilities.
The DRG Production Hardening Blueprint

Dev Resource Group deploys embedded Forward Deployed AI Engineers who integrate directly into your codebase to refactor experimental code into robust, hardened production infrastructure.

Our 4-Stage Production Delivery Framework

Stage 1: Architecture & Security Audit

Auditing existing prompt templates, embedding strategies, vector database configurations, and establishing private VPC boundaries with zero external data leakage.

Stage 2: Advanced Retrieval & Reranking

Replacing naive vector search with hybrid BM25 + dense embedding indexes, parent-child document chunking, and cross-encoder re-ranking algorithms.

Stage 3: Guardrails & Deterministic State

Implementing strict schema validators (Pydantic / Instructor), structured output state machines, and automated prompt injection protection layers.

Stage 4: Telemetry, Caching & CI/CD

Deploying semantic response caches (Redis), distributed tracing (OpenTelemetry), and automated synthetic evaluation pipelines to prevent regression drift.

Measurable Business & Engineering Outcomes

By embedding Forward Deployed Engineers directly alongside your team, we ensure that technical deliverables translate into tangible operational metrics:

  • Latency & Cost Optimization: Implementing multi-tiered caching, semantic routing, and token management to minimize latency and inference spend.
  • Deterministic API Schemas: Enforcing strict Pydantic models and structured outputs for reliable downstream database and UI contracts.
  • Groundedness & Citation Guardrails: Enforcing strict reference validation thresholds before returning model responses to end users.
  • Full In-House Transfer: Your internal engineering staff pairs with our FDEs, ensuring complete architectural comprehension upon handover.

Frequently Asked Questions

Why do over 90% of enterprise AI proofs of concept fail to reach production?
Prototypes operate on clean, static samples. Production demands dynamic chunking, real-time RBAC filtering, sub-second latency, deterministic output guarantees, and continuous regression monitoring that standard POCs completely lack.
How long does a DRG Pilot-to-Production sprint take?
Our focused engagements typically run between 4 and 8 weeks. In that window, our pod implements the data pipelines, builds production microservices, and integrates CI/CD evaluation suites.
How do you solve latency and cost overruns in production LLM systems?
We use multi-tier semantic caching, speculative decoding, task-specific distilled models (e.g. fine-tuned Llama 3 8B for classification), and asynchronous worker queues for non-blocking operations.

Take Your AI Initiative to Production

Assemble an agile engineering pod configured specifically for your AI stack and start shipping production code.

Configure Pod in Builder Request Architecture Review