RAG development and AI agent engineering for systems you can run.
We build enterprise knowledge systems, custom AI agents, and multi-agent workflows against your data and tools. Evaluation and retrieval observability are part of the job. Use a model when it earns its keep; use plain automation when that is enough.
From strategy to production systems.
Same engineering discipline across strategy, applications, retrieval, and agents—scoped to one production workflow first.
What changes
Before: models, docs, and ops tools live in separate silos.
After: retrieval, agents, and workflows connect to CRM, databases, phone, and email, with permissions and human oversight.
01 · AI Strategy & Architecture
Where AI earns its place
Map processes, data boundaries, and risk. Decide model vs rules, cloud vs private, and what a first production path should look like before you buy more tools.
02 · AI Application Engineering
LLM application development and model integrations
LLM APIs, structured outputs, routing, evaluation, and guardrails. Bedrock, Azure AI, Google Cloud, OpenAI, Anthropic, or a local model when that is what the constraint requires.
03 · RAG Development Services
Enterprise retrieval-augmented generation
Ingestion pipelines, chunking, embeddings, vector database, hybrid search, reranking, metadata filters, access control, citation, RAG evaluation, and retrieval observability. Production RAG on cloud, on-prem, or hybrid—grounded in your verified knowledge.
04 · Agentic AI & AI Agent Development
Custom AI agents and multi-agent orchestration
Retrieve, reason, call tools, write a record, verify, escalate. Multi-agent setups need shared state, clear handoffs, and an audit trail. We keep tool access narrow on purpose.
05 · Conversational / Voice AI
Voice and chat connected to workflows
Call and chat agents that capture intent, write a structured record, notify staff, and hand off. The phone path only matters if it lands in a system of record.
06 · Business Process Automation
Deterministic automation where it is enough
APIs, queues, routing, CRM write-backs, and exception boards. Combine with AI only where language, retrieval, or interpretation improves the outcome.
07 · AI Infrastructure & Deployment
Cloud, on-prem, or hybrid
Identity, networking, secrets, least-privilege IAM, observability, and model/data separation so systems stay operable after launch.
08 · Data & Integration Engineering
The pipes AI and automation depend on
ETL, reliable integrations, and analytics foundations so agents and dashboards are not guessing from stale spreadsheets.
Website and technical SEO work remain available as supporting capabilities when a conversion path or findability issue is part of the engagement. They are not Automatico’s primary identity.
What production RAG actually requires.
A vector store plus an LLM is a prototype. Production RAG needs disciplined ingestion, hybrid retrieval, access control at query time, and a way to measure when answers get worse. The stages below are where most demos go quiet.
Ingestion & data pipelines
PDFs, HTML, Markdown, code, and structured exports all have to become chunks with usable metadata. Production pipelines dedupe, re-index incrementally, and keep the corpus current. Stale documents are still the #1 way enterprise RAG quietly fails.
Hybrid search & reranking
Dense vectors alone miss product codes, policy IDs, and legal citations. We pair BM25 with embeddings, fuse the lists, then rerank the shortlist. You get recall from two signals and precision where it counts—before the model ever sees the context window.
Access control & governance
Permissions have to travel with the data. Document- and metadata-level filters at query time keep tenant and role boundaries intact. Without that, you built a search engine that ignores who asked.
RAG evaluation & retrieval observability
Precision, recall, faithfulness, citation accuracy—run against known-good questions. Traces should show embedding → search → rerank → generation so a bad answer points to a stage.
Start with one agent. Orchestrate when the workflow demands it.
Plenty of problems do not need an agent. When they do, the useful part is tool use, writes to real systems, and knowing when to stop.
Single-purpose agents
Classify a document, triage intake, redact PII, extract fields. Narrow tools, clear escalation. One agent, one workflow, something you can measure before you add a second.
Multi-agent orchestration
Research, draft, check policy, route: separate agents with shared state and handoffs. LangGraph or a custom state machine is fine; the requirement is that you can debug the pipeline when it stalls.
Human-in-the-loop
Confidence thresholds and approval gates. Agents should fail open to a person, not invent an answer. Log the tool calls so an auditor can replay the path.
Enterprise integration
CRM, ERP, ticketing, email, internal APIs. Retries, rate limits, and structured logs live here. An agent that cannot write a durable record is still a chat window.
Common questions before a first build.
What is the difference between RAG and fine-tuning?
RAG pulls documents at ask-time and grounds the answer. Fine-tuning changes model weights. If your knowledge moves often or you need citations, start with RAG. Fine-tune later for style or stubborn domain phrasing—if you still need it.
How long does a production RAG deployment take?
One corpus, one use case: usually 2–6 weeks from discovery to something running. Multi-tenant with access control and evaluation: closer to 6–12. We keep the first path small on purpose.
Do you build with LangChain, LlamaIndex, or custom?
Whatever fits. LangGraph when state machines help. LlamaIndex when ingestion is the hard part. Custom when the framework adds complexity without value. Framework choice follows the problem.
What does RAG evaluation actually measure?
Two buckets: did retrieval find the right chunks (precision@k, recall, MRR), and did generation stay faithful to them. We wire harnesses to known-good questions so quality is a measured number.
Can you deploy on our private infrastructure?
Yes. AWS, Azure, GCP, private DC, or hybrid. The patterns stay; the deployment target is config. We do not rewrite the product because the VPC moved.
What makes Automatico different from larger AI consulting firms?
You can open the reference builds. Real infra, real evaluation, small teams, fixed scope. Documentation and handoff are part of shipping.
Discovery. Fixed-scope build. Then expand.
No public price list. Scope depends on your data and constraints. The engagement shape stays the same.
Complimentary conversation on one workflow: stack, data boundaries, model vs rules, cloud vs private. You leave with a clear next step: build, wait, or do it yourself.
Usually a fixed fee after discovery. Typical first paths run days to a few weeks: RAG slice, intake agent, voice workflow, or document pipeline. Acceptance criteria agreed before work starts.
Runbooks, train-the-trainer, and a documented path to the next capability. Expand when the first path holds.
Included in a first build
- Scoped implementation in your target environment (or our reference infra for demos)
- Integration points agreed in discovery
- Basic observability and handoff docs
- Retrieval evaluation when RAG is in scope
What we will not do
- Chatbot wrappers with no system-of-record write path
- Invented case studies or unsupported ROI claims
- Unbounded retainers buried in the fine print
- Model selection theater without ingestion, access control, and evaluation
Larger multi-workflow systems are scoped individually: implementation, hosting assumptions, support, and change-control.
Production controls after launch.
Enterprise buyers ask how the system runs after launch. This is the baseline we design to.
Access & isolation
Least-privilege IAM, secrets outside source, private networking where the workload needs it, and no public upload buckets. Auth on demos that expose real retrieval paths.
Retrieval quality
Evaluation harnesses with known-good questions. Stage traces so a bad answer points to embedding, search, rerank, or generation.
Agent boundaries
Scoped tools, human escalation when confidence is low, and logs you can replay. Autonomy stops where your risk says it should.
Operability
Cloud, on-prem, or hybrid. Documentation and train-the-trainer so your team can run and extend what ships.
AI does not exist in isolation.
Models depend on data, identity, networks, storage, and the applications that already run the business. We build across those layers.
Integration & data movement
Reliable movement between ops systems and AI contexts—retries, alerts, and documentation your team can touch.
Security-conscious delivery
Least-privilege access, authentication, secrets management, audit trails, and human approval where the risk requires it. We only claim controls we implement or offer.
Request a consultation and note whether the priority is application, knowledge, or infrastructure.
Working systems you can inspect.
RAG platforms, voice agents, document workflows, structured intake, and engagement agents—with architecture and security evidence underneath.
Bring us the messy workflow and the knowledge behind it.
We will map one workflow and leave you with a clear next step. 945-353-5838 · sales@automatico.llc