Make AI work. Keep it working.
Abisam Solutions implements, corrects, and optimizes AI and machine-learning systems. From model behavior, memory, and data quality to architecture, infrastructure, and cost, we turn promising technology into dependable business systems.
Our team has delivered projects and training for organizations including Delta, MassMutual, GunBroker.com, Sigma Thermal, Comcast, and Deloitte.
The model is only one part of the system.
AI failures are frequently blamed on the model. In production, the cause is usually somewhere else: the data feeding the system, the way context is retrieved and assembled, the workflow around the model, or the infrastructure it runs on. Treating every problem as a model problem leads to expensive changes that do not fix the result.
Inconsistent results
The same input produces different answers, and quality drifts over time. Without evaluation baselines, no one can say whether a change made the system better or worse.
Weak context and memory
The system forgets what it should remember and retrieves the wrong material at the wrong time. Retrieval and memory are treated as afterthoughts rather than architecture.
Rising costs
Every task is sent to the largest model, context is stuffed instead of curated, and token spend climbs faster than value. Cost is discovered on the invoice, not designed for.
Production limitations
A system that worked in the demo wobbles under real traffic, real data, and real edge cases. Latency, monitoring, and failure handling were never built in.
Abisam diagnoses and improves the complete AI system, not just the visible symptom.
Three ways we make AI dependable.
AI Implementation
AI designed around a real workflow and delivered as an operational system, not a demonstration.
Learn moreAI Reliability and Optimization
More accurate behavior, lower operating costs, better performance, and fewer surprises after launch.
Learn moreData and Infrastructure Foundations
Reliable data and infrastructure that allow AI and ML systems to perform consistently at scale.
Learn moreAI expertise on both sides of production.
- Many firms specialize in building models but cannot make them dependable once real users arrive.
- Many firms specialize in infrastructure but cannot reason about model behavior, evaluation, or drift.
- Abisam works on both sides of production, so the intelligence and the system that carries it are accountable to the same team.
Two disciplines. One accountable team.
Abisam is run directly by its two founders. You work with the people doing the work, not a layer of account managers between you and the engineering.
Tim Lafferty
Tim leads the intelligence side of an engagement. His background is in statistics, mathematics, and machine learning, with a focus on why a model behaves the way it does. He works on model evaluation, hallucination and error reduction, drift detection, retrieval quality, and cost and latency optimization. When a system is inaccurate, expensive, or unpredictable, Tim finds the measurable reason and the smallest change that fixes it.
Bob
Bob leads the system side of an engagement. He brings more than 20 years of architecture and infrastructure experience across production deployment, system integration, performance, scalability, reliability, and cost management. His focus is the environment the model depends on: data pipelines, cloud architecture, APIs, and monitoring. When intelligence is sound but the surrounding system cannot support it, Bob makes the platform dependable, secure, and efficient.
Tim determines why the intelligence is not performing correctly. Bob ensures the surrounding system can support it reliably, securely, and efficiently.
Field notes on reliable AI.
How to Tell Whether Your AI System Is Drifting
Four kinds of drift degrade AI systems: model drift, data drift, LLM behavioral change, and silent vendor updates. Here is how to catch all four.
August 7, 2026
AI Memory Is Architecture, Not Magic
AI memory is not a model feature. It is a system you design: short-term context, long-term storage, retrieval, expiration, isolation, and a real cost curve.
August 5, 2026
The Model May Not Be Your AI Problem
Most production AI failures blamed on the model come from retrieval, data quality, context assembly, or workflow logic. Here is how to find the real cause.
August 3, 2026
Your AI problem may not actually be an AI problem.
Send us the system, workflow, or result that is not behaving. We will tell you where the real problem is, and what it would take to fix it.