Custom Enterprise AI Infrastructure.
Foundation > Features.

Every AI problem reduces to two questions: Did we have the right model? Did we feed it the right context?

Training custom models is possible but rarely the right move — it requires massive resources, and today's open-source alternatives underdeliver. That leaves one effective path: solve the context problem completely.

Custom retrieval infrastructure isn't a nice-to-have. It's step one.

While small apps can paste conversation history into prompts, enterprises face millions of documents, dozens of systems, regulatory constraints that change quarterly, and knowledge that expires monthly. Generic solutions break at this scale.

We build retrieval systems that understand your specific data topology — recognizing that the same query means different things in fashion versus electronics, that policy versions matter for compliance, and that access controls aren't optional.

This becomes the foundation for everything: customer-facing search that converts, employee knowledge systems that scale, AI agents that actually understand your business.

When retrieval is built right, it transforms from a technical requirement into the strategic infrastructure powering every AI initiative across your organization.

We make every customer interaction uniquely tailored, powered by systems that learn and adapt in real-time. True personalization used to be prohibitively expensive, requiring massive, perfectly structured datasets and complex engineering.

Generative AI has changed the rules. We leverage AI to build lightweight, powerful personalization engines that understand nuance from unstructured data — like product descriptions, user reviews, and even image aesthetics.

This allows us to deliver dynamic experiences once reserved for tech giants, from hyper-relevant content to adaptive pricing, all at a fraction of the traditional cost and complexity.

We deliver hyper-relevant advertising that finds the right audience at the right moment. We build intelligent advertising systems grounded in multi-armed bandit algorithms that balance exploration and exploitation — continuously testing new ads while automatically directing traffic to top performers.

AI enhances these classical techniques by making sense of complex user data in real-time — demographics, purchase history, and behavioral signals — to predict which ad will be most effective for each person.

This ensures your campaigns maximize click-through rates and ROI while continuously learning and improving.

We maximize customer lifetime value by delivering the right offer to the right user at the right time. A one-size-fits-all discount approach wastes margin on customers who would have stayed anyway while failing to retain price-sensitive users.

We build dynamic offer systems using adaptive experimentation frameworks that continuously test different retention strategies to identify the optimal approach for each segment. AI accelerates this process by analyzing behavioral patterns and user signals — from browsing to support interactions — that traditional rule-based systems miss.

The result: maximized retention rates while protecting your bottom line.

While ensuring the right context at the right time solves most enterprise needs, fine-tuning offers distinct advantages when specialized behavior justifies the investment: private deployment for data sovereignty, full budget control without per-token costs, and the ability to internalize domain-specific language patterns that context alone won't consistently capture.

The approach involves trade-offs. Enterprise API providers typically restrict fine-tuning to specific models rather than their latest releases, with limited algorithm options. Open-source models offer complete customization freedom and infrastructure control. Each path has merit depending on your deployment requirements, data sensitivity, and control needs.

Fine-tuning delivers measurable value in multi-agent systems where specific agents need business-specific instincts that context alone won't capture. The key is assessing each agent individually: RAG for most knowledge tasks, fine-tuning when domain language becomes the competitive edge.

Outcome: Context as foundation; fine-tuning as a strategic tool when business-specific behavior and deployment requirements justify the investment.

For controlled, deterministic tasks — classification, routing, scoring, and re-ranking — supervised models can outperform LLMs on speed, cost, and explainability in controlled business contexts.

Deploy these discriminative models when you need consistent, auditable outputs aligned with SLAs, governance, and compliance requirements.

For example, in re-ranking retrieved results, task-specific models often deliver faster and more stable ranking performance within enterprise retrieval stacks.

They integrate alongside retrieval and, where justified, fine-tuning — with the optimal choice determined by each function's performance targets and risk tolerance.