We engineer autonomous agents that won't break your workflows or hallucinate in production. Built with strict audit trails, bounded actions, and zero tolerance for unpredictable outputs.
Our Belief
In regulated environments, the hardest challenge isn't getting an AI to answer a question; it's guaranteeing that answer won't trigger a compliance violation, break a downstream system, or require a human to fix it.
We reject unpredictable agents that hallucinate under pressure. AI is not magic—it is software. It must be constrained, tested, and controlled just like any other mission-critical infrastructure.
Our Architecture
Most AI deployments fail when they hit edge cases in the real world. They make wrong decisions, break workflows, and create new operational risks. We build the safety nets that prevent those failures.
If an agent is allowed to guess, it will eventually guess wrong. We prevent this by enforcing strict rule schemas, explicit failure paths, and hard-coded workflow boundaries.
You can't fix what you can't see. We wrap every agent in live evaluation harnesses that catch hallucinated data, detect policy drift, and track every API call.
Autonomous does not mean unsupervised. We design clear escalation triggers so that edge cases are instantly routed to human experts before a mistake hits production.
When an agent makes a decision in a regulated space, you need to know exactly why. We build infrastructure that logs every token, reference, and reasoning step.
Deployed Systems
Agents operating within HIPAA environments. Automating clinical and administrative workflows with deterministic output validation.
Claims verification
Extract structured data from intake, cross-reference eligibility, and flag exceptions; with full audit trails.
Clinical documentation
Convert unstructured provider notes into coded, billable formats in real time with schema enforcement.
High-stakes document analysis with provenance tracking, hallucination controls, and human-in-the-loop escalation.
Contract risk extraction
Identify non-standard clauses, liability exposures, and renewal terms across multi-hundred-page agreements.
Precedent synthesis
Citation-grounded legal research with source verification and confidence-scored output generation.
Agents that resolve, route, and escalate complex support workflows; integrated directly with internal APIs.
Autonomous resolution
End-to-end L1/L2 ticket resolution with context retrieval, action execution, and policy-aware escalation.
Retention signaling
Behavioral drift detection across usage telemetry to trigger intervention workflows before churn.
Methodology
We execute a four-phase methodology designed for progressive confidence; starting with deep workflow mapping and ending with autonomous operation under continuous evaluation.
Mapping the operational bounds, failure modes, and required deterministic outcomes of the workflow.
Developing a constrained agent capable of executing the core workflow within simulated environments.
Running rigorous regression testing against historical data to establish baseline reliability.
Transitioning to active environments with human-in-the-loop oversight and continuous drift monitoring.
Foundation
When an LLM is wrong, it doesn't throw an error—it confidently lies. We embed decades of software testing expertise directly into our AI systems, ensuring models behave with absolute certainty instead of probability.
Shellexa wasn't born in an AI research lab; we were built on software quality assurance. We spent years building the frameworks that prevent mission-critical software from crashing. We bring that exact paranoia to AI.
Enterprise workflows break when AI is allowed to be creative. You cannot solve hallucination with better prompts; it is fundamentally a software testing problem. We test agents like we test banking infrastructure.
QA is not a final check before deployment—it is the core infrastructure. By wrapping every agent in continuous testing pipelines, we catch wrong decisions before they affect your business.
Capabilities
We don't just build agents; we build the testing environments they live in. Every action is measured against strict performance and safety baselines.
We prevent downstream failures by sandboxing models. If an agent encounters a scenario it cannot definitively resolve, it safely halts and escalates.
LLMs are useless if they can't touch your data. We engineer the secure plumbing required to connect agents to internal APIs and legacy databases.
Moving from prototype to production is where most AI projects die. We manage the entire transition, ensuring systems scale without degrading.
Security & Reliability
We operate within the strict security and observability standards that regulated industries demand. Not as an optional add-on, but as the core architecture of every deployment.
Agent workloads execute within your VPC or a dedicated tenant. No training on your private data. No risk of cross-tenant data leakage.
Every deployed agent runs against a live testing harness. We constantly measure accuracy, latency, and cost per operation—catching drift before users do.
Full decision trace for every action. We log exactly what data the agent saw and why it made a decision, formatted for immediate compliance review.
Agents ship with circuit breakers. If an API times out or a model hallucination is detected, the system gracefully degrades and escalates to a human.
Engagement
4 weeks
We characterize one workflow end-to-end: mapping bounds, feasibility, architecture, and delivering a proof-of-concept deployed in your environment.
Ongoing
Full agent engineering and operation. We handle the build, evaluation infrastructure, production deployment, and continuous monitoring.
Strategic
Long-term co-development of your AI platform. Dedicated engineering capacity, shared roadmap, and progressive autonomy transfer to your team.
Begin
We begin every engagement with a rapid assessment; mapping bounds, evaluating feasibility, and delivering a working proof-of-concept in your environment.