GPT-5 Dropped, but Bigger Context Windows Still Fail
Even with larger context windows, the core problem remains: long inputs degrade, compression distorts relationships, and costs rise.
We focus on foundational technologies that power the next generation of intelligent applications
Structures that keep facts straight and relationships intact, so your AI answers with context, not guesses.
SDKs and APIs that plug into your stack in minutes, not months. Now you can optimize GPT or Claude without upgrading models.
High-performance pipelines, observability, and guardrails that scale from a single dev to the whole org—without surprise costs.
Your AI scalpel that cuts token costs by 40% without upgrading your model. LLMs have a memory cap, the context window. Every extra token costs money and risks drift or hallucination. Contextus helps your AI prioritize what matters & discard the rest, allowing developers and teams to save 40 percent while improving accuracy.
Contextus Demo
AI Token Optimizer
How we got here & why we're obsessed with reliability.
We built for regulated markets: matching engines, reconciliation, and hard data problems. It taught us discipline, accuracy, latency, and cost control.
We’re applying those muscles to AI. First with Contextus, then a suite of developer tools that make large models practical in production.
Deep dives into emerging technologies, product development, and the future of software engineering.
Insights, research findings, and technical deep-dives from our team on AI, context engineering, and developer tools.
Even with larger context windows, the core problem remains: long inputs degrade, compression distorts relationships, and costs rise.
Prompt engineering has limitations. Context engineering is the next evolution for building reliable AI applications.
Deep dive into evaluation metrics and methodologies for Retrieval-Augmented Generation systems.