We design and deploy custom LLM pipelines, AI agents, and intelligent automation that transform how enterprises operate — at scale, with precision.
WEEKLY SAVINGS
$840K
WHAT WE BUILD
Precision-tune foundation models on your proprietary data for domain-specific performance.
Production-grade retrieval systems that keep your AI grounded in current, accurate knowledge.
Autonomous multi-step reasoning systems that execute complex workflows without human intervention.
Real-time visual intelligence for inspection, detection, and understanding at industrial scale.
Scalable ETL infrastructure that transforms raw enterprise data into AI-ready training sets.
End-to-end model lifecycle management — from experimentation to production monitoring.
HOW WE WORK
We conduct deep technical interviews with your engineering and product teams, mapping current systems, data assets, and AI readiness. You receive a comprehensive scope document within five business days.
Our senior AI architects design a system blueprint tailored to your infrastructure — covering model selection, data flow, latency requirements, and cost projections. No generic templates.
A working proof-of-concept ships in two to three weeks, letting you validate core assumptions before committing to full build. Stakeholders see real outputs, not slide decks.
Our engineering team implements, tests, and stress-tests the full system against your SLAs. Every deployment includes automated regression suites and rollback procedures.
We monitor production performance, retrain models as data drifts, and ship continuous improvements under an ongoing retainer. Your AI systems get smarter over time, not stale.
CLIENT RESULTS
"OaklineCrest reduced our model inference latency by 340ms, enabling real-time edge deployment we thought was years away. Their RAG architecture is the backbone of our entire product now."
THE TEAM
FROM THE LAB
General-purpose models cost more per token and underperform on domain tasks. Here is how we calculate the break-even point for custom fine-tuning across five industries.
Gemini 1.5 Pro and Claude 3.7 offer million-token contexts. We ran 14 benchmarks to find out when RAG still wins — and when you should just stuff the prompt.
Tool verification, grounded reasoning, and human-in-the-loop checkpoints are not optional in production. Here is our production playbook for reliable autonomous agents.
READY TO BUILD?
Most AI projects fail at implementation, not ideation. We close that gap — from first prototype to production scale.
