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Multi-Agent Orchestration · LLM Evaluation Guardrails · Production RAG

Enterprise AI & Machine Learning Services Engineered for Production ROI

TechAelia delivers enterprise AI, LLM integration, and machine learning pipelines that hold up in real operations, not demo notebooks. 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. We specialize in production RAG over private document corpora, multi-agent orchestration across ERP and CRM systems, and LLM evaluation guardrails that keep regulated UAE, UK, and EU clients confident that every response is traceable, policy-aligned, and cost-controlled. Engagements start with a discovery that maps data boundaries, retrieval quality targets, and success metrics so your first pilot ships with a clear path to production ROI.

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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 designed for auditability

We design agent systems for workflows that cannot rely on a black box. Task planners, tool-use loops, recursive self-correction, and full trace logs cover every step your operators need to defend in a compliance review or post-incident analysis. Each agent role is scoped with explicit permissions so ERP writes, CRM updates, and ticket actions stay predictable under load.

  • 15 to 40 step workflows across ERP, CRM, and ticketing systems with explicit handoff contracts
  • Sandboxed tool execution with deterministic schema validation using Zod or Pydantic
  • Recursive self-correction and secondary critic agents before responses ship to users
  • End-to-end trace logs for compliance, debugging, cost attribution, 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 stop out-of-policy responses before users see them. Quality, latency, and spend are tracked weekly so drift is caught before it becomes a support incident.

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

PRODUCTION RAG

Production RAG that respects access control

Enterprise retrieval is only useful when the right tenant sees the right chunk. We build hybrid search over million-document corpora with ACL-aware indexing, semantic chunking tuned to your document types, and reranking that lifts answer faithfulness without bloating context windows or inference cost.

  • Hybrid keyword and vector retrieval with tenant isolation across shared indexes
  • Chunking strategies tuned for policies, contracts, tickets, and structured tables
  • Rerankers and citation paths so operators can verify every grounded answer
  • Private VPC indexes with refresh jobs that keep knowledge current without full reingest
APPROACH

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

We decide per engagement, and many production systems combine both. RAG is usually the fastest path when policies and documents change often. Fine-tuning leads when tone, domain vocabulary, or structured output must stay consistent at scale. Here is how we choose which path leads when you scope enterprise AI work with TechAelia.

FactorRAG (retrieval)Fine-tuning
Best forKnowledge-heavy workflows, policies, and docs that change often and need citationsConsistent tone, domain vocabulary, and structured output at scale across channels
Data boundaryIndexes your documents in a private VPC while foundation model weights stay separateCurated training sets with model weights trained and hosted inside your cloud boundary
Time to pilotFaster for Q&A, search, and copilot-style workflows when source docs are readyLonger cycle that needs eval datasets, labeling discipline, and controlled retraining
TechAelia defaultOur default for enterprise knowledge workflows and regulated document copilotsAdded when tone or structure must stay 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.

Autonomous agents and LLM-powered systems built to automate enterprise workflows and deliver predictive insight, with evaluation, guardrails, and observability included from the start.

  • Custom LLM & RAG Pipelines

    Fine-tuning open-source LLMs such as Llama and Mistral alongside commercial models, paired with hybrid search databases for accurate context injection, citation trails, and tenant-safe retrieval in private cloud deployments.

  • Predictive Analytics

    Deep learning models built for anomaly detection, failure prediction, dynamic pricing systems, and real-time operations forecasts that plug into the same evaluation and monitoring stack as your LLM programs.

  • Computer Vision & NLP

    Custom OCR engines, semantic search pipelines, object detection models, and specialized natural language extraction setups for invoices, forms, and unstructured enterprise document corpora.

  • Autonomous Agent Orchestration

    Task planners, tool-use loops, recursive self-correction mechanisms, and visual web-crawlers running inside sandboxed environments with deterministic outputs your compliance team can audit.

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 with reproducible training pipelines.

  • Orchestration

    LangChain & LlamaIndex

    For reliable prompt-routing, multi-agent workflows, tool calling, and document semantic trees in production.

  • Data Stores

    PgVector & Milvus

    High-performance vector databases tuned for million-document RAG search with tenant isolation and ACL filters.

  • Backend

    FastAPI / Python

    Ultra-fast, asynchronous REST endpoints serving model inferences with minimal lag and structured JSON schemas.

PROCESS

Production pipeline

How we move from architecture to live operations.

  1. 01

    Phase 01 · Weeks 1-2

    Architecture Discovery

    We chart your semantic context, APIs, document access models, and success metrics to set clear data and latency boundaries before any model work starts.

    Deliverables

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

    Phase 02 · Weeks 3-5

    Secure RAG Setup

    We ingest your dataset, refine semantic chunking for policies and tickets, and stand up private vector indexes inside your VPC with ACL-aware retrieval.

    Deliverables

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

    Phase 03 · Weeks 6-9

    Agent Integration

    We deploy multi-agent task loops, sandboxed tools, and Pydantic or Zod structured outputs wired to ERP, CRM, and ticketing systems.

    Deliverables

    • Asynchronous agent engine
    • Interactive system dashboard
  4. 04

    Phase 04 · Weeks 10-12

    Automated Evaluation

    We configure LLM-as-judge automated testing, golden-question regression, and load tests so inference quality and response times stay within agreed budgets.

    Deliverables

    • Performance eval scorecard
    • Production release train
WHY TECHAELIA

Why Choose 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 so every answer cites the right source without leaking across business units.

  • Multi-Agent Swarms

    Orchestrated agents that automate 15 to 40 step workflows across ERP, CRM, and ticketing systems with complete trace logs, schema-validated tool calls, and rollback hooks when a step fails validation.

  • Rigorous Safety Guardrails

    Token budgets, PII filters, and LLM-as-judge evals that block 99%+ of out-of-policy responses before they reach users, with weekly quality and cost reviews after go-live.

PROOF

Related case studies

Real outcomes from our ai / ml practice.

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FAQ

Common questions

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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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