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Multi-Agent Orchestration · LLM Evaluation Guardrails · Production RAG

Enterprise AI & Machine Learning Services Built for Production ROI

TechAelia delivers enterprise AI, LLM integration, and machine learning pipelines that survive real operations. Teams we partner with typically cut manual workflow time by 40% to 75% within 90 days, run sub-300ms RAG queries at scale, and launch pilots in 8 to 12 weeks with private VPC deployment, audit logs, and automated eval gates included, including private-cloud deployments for regulated UAE, UK, and EU clients.

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Enterprise AI & Machine Learning

  • 99.2%

    Pilot Accuracy

  • 75%

    Less Manual Work

  • 250ms

    RAG Latency

MULTI-AGENT ORCHESTRATION

Multi-agent orchestration, built for auditability

We design agent systems for workflows that cannot afford a black box: task planners, tool-use loops, recursive self-correction, and full trace logs across every step your operators need to defend in a review.

  • 15 to 40 step workflows across ERP, CRM, and ticketing systems
  • Sandboxed tool execution with deterministic schema validation (Zod/Pydantic)
  • Recursive self-correction and secondary critic agents before responses ship
  • End-to-end trace logs for compliance, debugging, and post-incident review

LLM EVALUATION

LLM evaluation and guardrails engineering

Most agencies mention AI safety in one vague sentence. We engineer the mechanism: automated eval suites, token budgets, PII filters, and LLM-as-judge gates that block out-of-policy responses before users see them.

  • LLM-as-judge evals with golden-question regression suites
  • Token budgets and PII filters that block 99%+ of out-of-policy responses in pilot
  • Latency, cost, and quality dashboards tracked weekly after launch
  • Human-in-the-loop review queues for edge cases, not every routine answer
APPROACH

RAG vs. fine-tuning: which fits your use case?

We choose per engagement, and many production systems combine both. This is how we decide which path leads when you are scoping enterprise AI work.

FactorRAG (retrieval)Fine-tuning
Best forKnowledge-heavy workflows, policies, and docs that change oftenConsistent tone, domain vocabulary, and structured output at scale
Data boundaryIndexes your documents in a private VPC; weights stay separateCurated training sets with model weights in your cloud boundary
Time to pilotFaster for Q&A, search, and copilot-style workflowsLonger cycle; needs eval datasets and controlled retraining
TechAelia defaultOur default for enterprise knowledge workflowsAdded when tone or structure must be consistent; combined with RAG when both matter

TechAelia · Stack

CAPABILITIES

What we build under this service

Engineering depth across enterprise ai & machine learning, from discovery through production handoff.

Intelligent autonomous agents and LLM-powered systems engineered to automate enterprise workflows and surface predictive insight, with evaluation, guardrails, and observability built in from day one.

  • Custom LLM & RAG Pipelines

    Fine-tuning open-source LLMs (Llama, Mistral) and commercial models, combined with advanced hybrid search databases to ensure accurate context injection.

  • Predictive Analytics

    Deep learning models developed for anomaly detection, failure prediction, dynamic pricing systems, and real-time operations forecasts.

  • Computer Vision & NLP

    Custom OCR engines, semantic search pipelines, object detection models, and specialized natural language extraction setups.

  • Autonomous Agent Orchestration

    Task planners, tool-use loops, recursive self-correction mechanisms, and visual web-crawlers built using sandboxed sandboxes.

ENGINEERING

Tools and platforms

Modern, vetted stack choices for build, scale, and observability.

  • Frameworks

    PyTorch & Transformers

    For building, evaluating, and fine-tuning custom deep learning and LLM models.

  • Orchestration

    LangChain & LlamaIndex

    To construct reliable prompt-routing, agent workflows, and document semantic trees.

  • Data Stores

    PgVector & Milvus

    High-performance vector databases optimized for million-document RAG search.

  • Backend

    FastAPI / Python

    Ultra-fast, asynchronous REST endpoints serving model inferences with minimal lag.

PROCESS

Production pipeline

How we move from architecture to live operations.

  1. 01

    Phase 01 · Weeks 1-2

    Architecture Discovery

    We map your semantic context, APIs, and document access models to define boundaries.

    Deliverables

    • Context map & system specifications
    • Model sizing study
  2. 02

    Phase 02 · Weeks 3-5

    Secure RAG Setup

    We ingest your dataset, optimize semantic chunking, and spin up private vector indexes.

    Deliverables

    • Configured hybrid vector db
    • Initial retrieval precision audit
  3. 03

    Phase 03 · Weeks 6-9

    Agent Integration

    We deploy multi-agent task loops, tools, and Pydantic/Zod structured outputs.

    Deliverables

    • Asynchronous agent engine
    • Interactive system dashboard
  4. 04

    Phase 04 · Weeks 10-12

    Automated Evaluation

    We set up LLM-as-judge automated testing and load test inference response times.

    Deliverables

    • Performance eval scorecard
    • Production release train
WHY TECHAELIA

Why TechAelia AI Engineering?

What sets our delivery apart on engagements like yours.

  • Context-Optimized RAG

    Hybrid retrieval across 1M+ document chunks with tenant isolation, 95%+ retrieval precision in pilot audits, and strict ACL-aware indexing.

  • Multi-Agent Swarms

    Orchestrated agents that automate 15 to 40 step workflows across ERP, CRM, and ticketing systems with full trace logs.

  • Rigorous Safety Guardrails

    Token budgets, PII filters, and LLM-as-judge evals that block 99%+ of out-of-policy responses before users see them.

PROOF

Related case studies

Real outcomes from our ai / ml practice.

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FAQ

Common questions

Timelines, security, and how we deliver enterprise ai & machine learning with your team in the loop.

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  • We utilize isolated cloud VPCs, private endpoints, and support fully on-premise/private cloud deployments of open-source models (such as Llama-3 or Mistral). Your data never trains third-party public models.

  • We employ a multi-layered verification strategy: strict semantic chunking, prompt-caching verification, deterministic schema validation (using Zod/Pydantic), and real-time secondary critic agents.

  • Our standard discovery and initial proof-of-concept (POC) takes 2 to 4 weeks. A production-ready, fully-integrated enterprise pilot is typically shipped in 8 to 12 weeks.

  • Yes. We build secure API connectors, event-driven sync jobs, and retrieval layers that respect your source-of-truth systems. Integrations are scoped during discovery so permissions, audit logs, and rollback paths are defined before go-live.

  • We choose per use case. RAG is our default for knowledge-heavy workflows. Fine-tuning is recommended when you need consistent tone, domain vocabulary, or structured output at scale. Many engagements combine both with evaluation gates before production.

  • We ship with automated eval suites: golden-question sets, regression checks on structured outputs, latency and cost dashboards, and human-in-the-loop review queues for edge cases. Success metrics are agreed in discovery and tracked weekly.

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