Service Overview
Turning models into systems that are measurable, operable, and safe to ship.
Model quality degrades in production without retrieval quality, evaluation discipline, and safety controls.
Key Capabilities
The capabilities we use to solve the problem and keep the system operable.
Build sophisticated retrieval-augmented generation systems with vector databases and semantic search.
Implement robust guardrails, evaluation frameworks, and safety protocols for AI systems.
Optimize model inference with serverless architectures and performance monitoring.
Comprehensive monitoring and evaluation of model performance, drift, and business impact.
Our Approach
Retrieval pipelines, eval harnesses, prompt and inference orchestration, and monitoring.
Deliverables
Deliverables, documentation, and operating guidance designed to stay useful after delivery.
Optimized semantic search infrastructure with hybrid retrieval (dense embeddings + BM25) and automatic re-indexing
Hallucination rate analysis, bias audits, and adversarial prompt testing with pass/fail criteria
Version-controlled prompt templates with A/B test results and regression benchmarks
Auto-scaling inference setup with cold-start optimization, latency budgets, and cost projections
Step-by-step procedures for safe model swaps, canary deployments, and rollback triggers
High-performance vector storage for semantic search and similarity matching
Model evaluation, hyperparameter search, and dataset versioning
Scalable, cost-effective model deployment with automatic scaling
Automated prompt regression testing, guardrail validation, and quality scoring
Agent workflows, tool-use routing, and retrieval chain composition
Results
A B2B SaaS platform needed to reduce support ticket volume by enabling customers to self-serve from a 12,000-page technical documentation library. We designed and delivered the following:
01
Built hybrid retrieval pipeline indexing 12,000+ pages — dense embeddings for conceptual queries, BM25 for exact terms, with automatic re-indexing on documentation updates
02
Implemented prompt orchestration with source attribution — responses cite specific documentation sections, reducing hallucination rate to under 2% in production
03
Deployed on serverless inference with sub-800ms P95 latency — auto-scaling from 0 to 200 concurrent sessions, cold-start optimized with model warmup
04
Achieved 32% improvement in first-answer accuracy — average ticket resolution time dropped from 4.2 hours to 1.1 hours, deflecting 40% of L1 support tickets
CONTACT
We design AI systems that remain useful after launch.
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