➜ ./webnuvexa --ai
Initializing model gateway▊
Indexing evaluation harness...
✓ OpenAI + Claude providers connected
✓ Retrieval layer mounted
✓ Agent runtime armed
Ready to reason▊
// AI Development
Custom LLM applications, autonomous agents, and machine learning systems built on OpenAI and Claude — engineered with evals, guardrails, and cost controls, not demos that die in staging.
Four AI modules, one production pipeline. Every model call traced, scored, and costed.
Custom applications on GPT and Claude with retrieval-augmented generation, structured outputs, and regression-tested prompt suites.
Tool-using agents that plan multi-step work, call your real APIs, and pause for human approval on anything irreversible.
Custom training pipelines, feature stores, and low-latency model serving for classification, ranking, and forecasting.
Document understanding, semantic search, and voice pipelines layered over your proprietary data and internal systems.
Everything between "we should use AI" and a feature your users actually trust.
We map where AI creates measurable margin — and where it quietly does not — before a single line of code.
OpenAI, Anthropic Claude, Mistral, and open-weight models behind one typed gateway with routing, caching, and retries.
Chunking, embeddings, hybrid search, and reranking tuned against your own documents so answers cite real sources.
Tool definitions, planner loops, memory strategy, and deterministic fallbacks for the requests a model should not handle.
Golden datasets, LLM-as-judge scoring, and CI gates that block prompt or model regressions before customers see them.
Supervised fine-tuning and LoRA adapters when prompting alone cannot reach the accuracy bar.
Prompt caching, batch processing, model tiering, and per-feature spend caps so unit economics hold at scale.
PII redaction, prompt-injection defenses, audit logging, and documented human review paths.
A staged process that de-risks the model before it reaches your customers.
Define the decision the model makes, the baseline it must beat, and the metric that proves it worked.
Audit, clean, and structure the corpora. Build the golden evaluation set before writing prompts.
Baseline model comparison across prompts, RAG configurations, and model tiers. Kill weak ideas early.
Guardrails, injection defenses, latency tuning, and an eval suite wired into CI.
Observability dashboards, cost budgets, staged rollout, and ongoing accuracy monitoring.
Model-backed features running live, with real usage numbers behind them.
Support copilot that drafts agent replies from ticket history and internal runbooks, citing the source passage for every suggestion.
Document AI for invoice processing: an extraction pipeline with confidence thresholds and a human review queue for low-confidence fields.
Personalization engine ranking feeds per user, with an A/B harness that evaluates model candidates before anything is promoted.
Voice agent that qualifies inbound calls, books meetings, and writes structured transcripts back to the CRM.
Initialize your project. We'll respond within 24 hours.