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Build production-oriented applications, integrations, workflows, evaluations, and intelligent automation.
Building a proof-of-concept AI prototype is easy; engineering a production system that performs reliably under real-world enterprise traffic is hard. Evergreen serves as your hands-on implementation partner, engineering containerized microservices, workflow automations, enterprise API integrations, and continuous evaluation pipelines across our complete engineering lifecycle: Discover → Architect → Build → Validate → Scale.
We focus on solving concrete engineering, data, and organizational bottlenecks rather than speculative generative AI hype.
Over 80% of enterprise AI pilots remain trapped in experimental notebooks because they lack production software architecture, error recovery, and enterprise integration.
Production applications suffer from unpredictable API latency, rate limit throttling, and uncontrolled token consumption that drains operational budgets.
Engineering teams have no automated way to verify whether a model prompt update or version migration breaks downstream business logic or degrades output quality.
Custom AI scripts fail to interface cleanly with legacy ERP, CRM, and mainframe backends, creating brittle point-to-point connections that break under load.
Organizations operate AI systems blindly without real-time telemetry tracking token consumption, latency distributions, error rates, or model behavioral drift.
Containerized, scalable AI microservices engineered with automated retries, semantic caching, circuit breakers, and graceful fallback models.
Slashing recurring token spend by 40% to 60% through semantic query caching, prompt compression, and intelligent model routing.
Continuous testing suites that evaluate prompt changes, guardrails, and model updates against standardized test datasets before deployment.
Seamless, secure API connectors linking modern AI capabilities directly into core business workflows, ticketing queues, and databases.
Full-stack OpenTelemetry instrumentation providing real-time visibility into latency, cost-per-call, error rates, and input/output distributions.
Evergreen approaches AI application development as a systems engineering discipline. We build modular, well-tested codebases designed for long-term maintainability, complete client source code ownership, and deterministic reliability.
Discover → Architect → Build → Validate → Scale. We make 'Validate' a dedicated, rigorous milestone prior to production scaling.
Rigorous adversarial fuzzing, token stress testing, latency profiling, schema contract validation, and human sign-off before production cutover.
Deploying local vector caches to instantly serve frequent queries, bypassing expensive external model calls and drastically reducing latency.
Source-code ownership and handover models available based on engagement structure, designed for operational autonomy without proprietary platform lock-in.
Every client engagement produces tangible architectural artifacts, verifiable codebases, or defensible strategic roadmaps.
Developing containerized backend services (TypeScript, Python, Go) implementing robust AI-driven business logic.
Automating multi-step operational workflows such as document routing, claims intake, invoice reconciliation, and customer triage.
Building robust, authenticated API middleware connecting modern AI endpoints to enterprise ERP, CRM, and SQL/NoSQL data layers.
Benchmarking and integrating the optimal mix of open-weights models (quantized for on-premise execution) and commercial cloud APIs.
Integrating automated test suites into CI/CD pipelines to validate prompt updates and model revisions against regression benchmarks.
Implementing end-to-end tracing and monitoring to track latency, token consumption, error rates, and semantic drift across all endpoints.
Deploying semantic caching layers, prompt compression techniques, and model tiering to minimize compute overhead and API expenses.
Deterministic architectures enforcing data perimeter containment, least-privilege tool execution, and clear trust boundaries.
Disciplined progression featuring a visibly differentiated Validation milestone.
Workflow audit, stakeholder interviews, data inventory, and SLA definition.
Zero-trust topologies, vector schemas, API contracts, and compute sizing.
Modular microservices, workflow logic, semantic caches, and tool integrations.
Adversarial fuzzing, schema enforcement, latency stress tests, and human sign-off.
Kubernetes orchestration, telemetry dashboards, workforce handover, and documentation.
Moving from experimental prototypes to enterprise-grade operations requires structured execution. We treat Validate as a distinct, mandatory verification milestone before any system reaches production scale.
Opportunity & Baseline
Audit current data hygiene, assess key areas of technical readiness, evaluate business feasibility, and calculate token economics.
Trust & Systems Topology
Define zero-trust network perimeters, vector index schemas, least-privilege tool contracts, and compute infrastructure sizing.
Software & Integrations
Engineer modular microservices, retrieval-augmented generation pipelines, finite-state agents, and legacy database connectors.
RIGOROUS VERIFICATION
Adversarial prompt fuzzing, hallucination rate benchmarks, latency stress tests, deterministic schema validation, and human sign-off gates.
Production & Ownership
Deploy to client VPC or on-premise Kubernetes clusters with OpenTelemetry observability, semantic caching, and source-code handover models available based on engagement structure.
Most AI failures happen when companies skip directly from "Build" to "Scale". Evergreen runs automated regression test suites, hallucination stress tests, and token cost circuit-breakers before any code touches live operational databases.
Informed by IEEE software engineering standards, DevSecOps principles, and NIST AI RMF MEASURE and MANAGE functions.
Enforced Validation Controls:Evergreen delivers production software engineering and implementation services. Systems are deployed into client-owned infrastructure, with source-code ownership and operational handover models structured according to commercial agreement.
Strictly classified in accordance with Evergreen's published claim-governance rules.
Context: Extensive platform engineering and systems optimization leadership demonstrated by Dr. Felix Njeh across high-density computing and federal modernization environments.
Approach: Architected resilient distributed cloud systems, database optimization, and high-availability disaster recovery infrastructures.
Outcome / Spec: Demonstrated track record of enterprise computing reliability and systems architecture rigor.
Context: Evergreen engineered a reference architecture demonstrating local data-masking middleware designed to limit PII transmission prior to model query dispatch.
Approach: Client-side token interceptor paired with deterministic regex and NLP masking rules operating within local processing boundaries.
Outcome / Spec: Reference architecture blueprint published in the Evergreen technical library.
Initiate a confidential technical consultation with our engineering and strategy team. All inquiries are routed directly to Senior Principal Dr. Felix Njeh and our technical architecture practice.
Confidential intake • Zero client data used for model training • Austin, Texas • Nationwide Enterprise & Public Sector Deployments