Generative AI
Production-grade generative AI integrated securely into core enterprise software.
We build domain-tuned foundation model pipelines, fine-tuned embeddings, and multi-modal generative interfaces that solve concrete business challenges with strict privacy and deterministic guardrails.
Moving beyond fragile prompts to hardened production AI
Prototype LLM demos often fail in enterprise production due to hallucinations, unpredictable latency, privacy leakage, and unbounded token costs. Enterprise environments demand deterministic guardrails, schema enforcement, and private hosting.
Hallucinations and non-deterministic schema violations crashing downstream systems
Data privacy concerns when transmitting proprietary IP to public API endpoints
Runaway token consumption and unbounded inference latency under production load
Difficulty evaluating and testing prompt regressions systematically across releases
Generative AI Capabilities
Deterministic Output Structuring
Forcing model responses into strict JSON and Pydantic schemas with automatic validation, retry parsing, and fallback paths.
Private & Local Model Deployment
Deploying quantized open-weights models (Llama, Mistral, Qwen) on private VPC infrastructure with zero external data transmission.
Fine-Tuning & LoRA Adaptation
Adapting base models on proprietary domain corpora using parameter-efficient fine-tuning (PEFT/LoRA) for specialized terminology.
Evaluation & Regression Benchmarks
Automated benchmark test suites measuring accuracy, latency, and hallucination rates before prompt changes reach staging.
Proven runtimes and industry-standard protocols.
We select tools based on operational reliability, long-term maintainability, and horizontal scaling capabilities.
Model Runtimes
Orchestration & Schemas
Cloud & Acceleration
Evaluation & Tracing
Why companies choose CodeSelf.
We operate as your dedicated technical partner with production codebases and zero markups.
What you receive at handoff.
Every engagement concludes with documented, tested, and deployable production codebases with zero ongoing licensing fees.
How CodeSelf builds and delivers generative ai.
Sprint-based delivery with weekly code releases directly into your private staging environment.
Task Framing & Dataset Curation
Identify domain tasks suitable for generative AI vs deterministic rules and curate golden test datasets.
Architecture & Model Selection
Benchmark commercial foundation models against private self-hosted open models for latency, quality, and cost.
Guardrails & Schema Rigor
Implement input sanitation, output validation, rate limiting, and defensive fallback routes.
CI/CD Evaluation & Monitoring
Establish automated regression pipelines testing prompt drifts against canonical ground-truth datasets.
Key technical and commercial questions.
Who owns the source code and intellectual property for generative ai systems?+
Can this technology stack be deployed inside our private cloud infrastructure (AWS/GCP/Azure)?+
How do you ensure zero downtime when updating live production services?+
What happens after the project launch? Do you provide ongoing maintenance and handover?+
Related technologies in our stack.
AI Agents
We build goal-driven AI agents that parse complex workflows, interact with enterprise APIs, verify intermediate state, and execute mission-critical tasks with robust human-in-the-loop oversight.
RAG / Knowledge Systems
We build enterprise RAG systems featuring hybrid semantic search, chunk-aware reranking, recursive document summarization, and strict provenance citation to eliminate hallucinations completely.
Computer Vision
We design and deploy deep learning vision models for automated quality control, document intelligence, object detection, and real-time camera stream analytics optimized for both cloud and edge hardware.
Ready to build or scale your generative ai stack?
Schedule an architectural consultation with our senior systems team to evaluate infrastructure, tooling, and delivery roadmaps.