Aixcel Labs · Agentic AI & LLM Systems Specialist
Aixcel Labs turns agentic AI architecture into inspectable public proof: typed contracts, deterministic gates, explicit agent state, evidence, approval, evaluation, replay, and deployment.
A production-minded agentic system is not a free-form conversation. It has typed inputs, bounded tools, durable state, measurable tests, observable failures, human authority, and a replay path when a provider is unavailable.
Each system has a live deployment, source repository, typed API contract, Postman collection, evaluation fixtures, architecture diagrams, and a replay path.
01 · Verified public demoFive bounded roles turn an objective and budget into a ranked creator plan, expose every decision, and stop before outreach or spend.
Measured proof: 5 graph roles, 3 scenario shapes, objective-sensitive ranking, 0 automatic external actions
Open live system →
02 · Verified public demoA measurement council reconciles platform aggregates, separates observation from causality, and blocks claims without valid denominators or evidence.
Measured proof: 5 measurement roles, 3 data-quality scenarios, 4 attribution windows, 100% golden scenario evidence coverage
Open live system →
03 · Verified public demoA timed script becomes culturally reviewed Urdu, Roman Urdu, or Arabic copy with distinct voice registers, safety flags, and native-language approval.
Measured proof: 3 source scenarios, 3 locale routes, 3 voice registers, 27 meaningful combinations
Open live system →
04 · Verified public demoA live black-box evaluator probes deployed systems for contracts, evidence, approval gates, idempotency, boundaries, and latency, including labelled fault injection.
Measured proof: 3 target deployments, 7 weighted checks, 4 baseline and fault scenarios, arbitrary URLs blocked
Open live system →
05 · Verified public demoA reproducible historical baseline returns forecast ranges, confidence, cohort fallback, and input sensitivity before a post is published.
Measured proof: 500 licensed public records, 400 training rows, 100 holdout rows, versioned ridge models
Open live system →Graph orchestration is used where state and branching matter. Deterministic code owns scoring, evidence rules, limits, and safety gates. Human authority remains visible.
Python 3.12, FastAPI, Pydantic v2, REST, generated OpenAPI, and LangGraph for the campaign decision graph.
Objective-sensitive ranking, attribution assumptions, locale and register controls, model ranges, idempotency, replay, and explicit approval states.
Pytest, Postman collections, Playwright, GitHub Actions, Vercel, X-Trace-ID headers, structured JSON logs, and black-box evaluation.
The live portfolio uses synthetic or licensed public records and performs no client-system mutation. It demonstrates architecture and behavior, not client production acceptance.
Objectives alter creator ranking, attribution windows alter calculated outcomes, language and tone alter reviewed copy, faults alter evaluation scores, and content inputs alter forecast ranges.
Responses expose typed state, traces, evidence, assumptions, limits, approval state, latency, usage, and a request trace ID.
Green CI, a live URL, or a score of 100 proves the tested portfolio artifact only. Real integration still needs private data review, staging UAT, named owners, rollback, and cost approval.
LangChain, CrewAI, MCP, a vector database, and Kubernetes remain outside a project unless the real data flow needs them and their operation can be demonstrated.
Compare adjacent systems, inspect evidence, or see how Aixcel delivers the work.
In 25 focused minutes, we will map where work or revenue is getting stuck, test whether AI is the right intervention, and identify the highest leverage first step.
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