Domain-Specific Harnesses for
Your Internal Processes.
Our core service: we design and build the harnessed orchestrator that runs one of your processes—accounts payable, intake, stock replenishment—and wire it to your systems through MCP servers. A person used to run that step by hand; a harness now runs it with your policy, your checkpoints, and a trail of every run. Every other node of the Context Layer Pipeline—frontend policy enforcement, privacy-filtered storage, isolated compute, RL optimization—is also available as an independent engineering service.
WHAT WE OFFER
The Harness First. Then Any Node, or the Full Flow.
Our core service is a domain-specific harness for one of your internal processes. Around it, every component of Eikasia's Context Layer Pipeline is available as an independent engineering service. You don't need to replace your existing architecture—we wire into your exact backend and frontend setup.
Harnessed Orchestrator
We design a domain-specific harness for one of your internal processes and build it end to end. We map the manual step or the handoff it replaces; define the tools as your operations, the policy as your manual, the checkpoints where a human must approve, the verification that says when a run is done and correct, and the trail that logs every run. Then we convert your existing backend infrastructure and business logic into the typed MCP servers the harness acts through: schemas, custom tool connectors, and multi-agent orchestration where the process needs it.
PIPELINE SUBPARTS
Frontend Interface
We build and wire user interfaces to securely capture client intent. Combining traditional Web APIs (REST/GraphQL) with modern MCP-UI protocol bindings, we ensure user actions are client-validated before dispatch.
Cloud Storage
We integrate A2DB privacy middleware into your cloud data layer. Sensitive enterprise records and customer PII are tokenized and scrubbed before entering external model context windows.
Cloud Compute
We wire non-deterministic model decisions to deterministic compute environments. We deploy sandboxed runners, asynchronous task dispatching, and secure API bridges for production code execution.
Optimization Engine
We capture runtime execution logs to continuously optimize your pipeline. Using Reinforcement Learning policy tuning and cost/latency benchmarking, we eliminate failure loops and lower token usage.
CORE CAPABILITIES
How We Engineer Your Harness and Pipeline
We bridge the gap between experimental AI prototypes and enterprise-grade software: a harness designed for your process, secured by the context layer, and tuned in production.
Domain-Specific Harness Design: Traditional APIs + Modern MCPs
We start from the process, not the model: which manual step or handoff the harness takes over, what state it reads, what tools it may call, where a human must approve, and what counts as done. Most enterprise apps rely on existing REST, GraphQL, gRPC, or SQL connections, so we build the unified adapters that expose these legacy systems to the harness as typed Model Context Protocol (MCP) servers and MCP-UI interactive frontends, without rewriting your core backend.
- ›Process mapping: the step or handoff the harness replaces
- ›Harness loop design: state evaluation, tool dispatch, human checkpoints
- ›Custom MCP server development for legacy databases & microservices
- ›MCP-UI integration for rich, interactive model-driven client components
- ›Type-safe protocol definitions with runtime validation
- ›Unified REST / GraphQL to MCP translation layers
Context Layer Infrastructure & Security
Connecting LLMs to production requires deterministic boundaries. We deploy privacy-filtered storage middleware (A2DB) and dedicated policy engines to ensure models act only within authorized operational limits.
- ›Zero-Trust PII scrubbing & tokenization middleware
- ›Real-time policy engine setup for hard constraint enforcement
- ›Granular RBAC & permission boundaries for AI agents
- ›State synchronization between frontend UI and cloud compute
Pipeline Optimization & Latency Control
AI pipelines quickly become slow and expensive without optimization. We analyze execution logs, fine-tune model parameters, and implement RL feedback loops to maximize accuracy while minimizing latency and API billable costs.
- ›Execution log telemetry & anomaly detection
- ›RL policy tuning for multi-step agent decision loops
- ›Context compression & intelligent prompt caching
- ›Latency and cost benchmarking across major LLM providers
ENGAGEMENT MODELS
Flexible Scoping Tailored to Your Stack
Whether you need one process harnessed, a targeted intervention on one node, or a full-stack context pipeline implementation, we fit seamlessly into your engineering roadmap.
One Process, One Harness
The default engagement. Pick one internal process—one workflow, one owner, one approval policy, one trace—and we deliver the domain-specific harness that runs it, wired to your systems. The same scope also covers targeted upgrades to existing AI infrastructure: an individual pipeline node such as an A2DB privacy proxy or an MCP-UI agent component.
- › Process mapped, harness delivered, checkpoints where you want them
- › Drop-in replacements for failing pipeline segments
- › Rapid execution (1 - 4 week sprints)
- › Hand-off and training for your internal devs
Dedicated Pipeline Engineers
A team of Eikasia specialists embedded in your organization. For when several processes need harnessing, or the whole pipeline. We handle the entire integration lifecycle, from auditing legacy APIs to running domain-specific harnesses and policy engines in production.
- › Ideal for complex or regulated environments
- › Continuous RL tuning and latency monitoring
- › Direct line to Eikasia core infrastructure team