Generative AI

GenAI that makes it to production — and stays there.

Most AI pilots stall in a notebook. Devotica builds the platform underneath them: retrieval grounded in your own data, agents that do real work, and the evaluation and ops to run it safely. Built on Amazon Bedrock and model-agnostic underneath — never locked to one model.

AI flow

Grounded AI, built around your data.

We connect documents, databases, and knowledge graphs into a retrieval pipeline so models answer from your data, not generic training data.

  • High-quality vectors from your docs and metadata
  • Model-agnostic retrieval with citation-aware prompts
  • Guardrails, access controls, and monitoring baked in
rag.py · grounded GROUNDED RETRIEVAL-AUGMENTED · MODEL-AGNOSTIC embed context answer retrieve Sources DOCS · DBS · APIS Vector store EMBEDDINGS LLM BEDROCK · OPENAI App CHAT · AGENTS
What we do

From idea to a system you can rely on.

Four capabilities that take generative AI from demo to dependable, on whichever cloud and models you prefer.

LLM & RAG Apps

Answers grounded in your data, not the model's guesses

We connect large language models to your documents, tickets and databases through retrieval — so responses are accurate, current and cited. The difference between a demo that hallucinates and a tool people actually trust.

  • Retrieval over your own knowledge, with source citations
  • Access controls so users only see what they should
  • Model-agnostic — swap providers without a rewrite
rag — query
$ devotica rag query "refund policy for EU?"
retrieve: 6 chunks · 38ms
rerank: top-3 selected
generate: grounded · 2 citations
guardrail: no PII leaked
# answer cites policy.pdf §4.2, eu-terms.md
AI Agents & Automation

Agents that do the work, with a human in the loop

Beyond chat: agents that query systems, draft and execute actions, and hand off to people when it matters. We wire in tools, guardrails and approvals so automation is useful without being reckless.

  • Tool and API access scoped with least privilege
  • Approval gates for anything irreversible
  • Full audit trail of every action an agent takes
agent — run
plan classify support ticket
tool lookup order #88123
tool check refund eligibility
gate >$500 → human approval
act draft reply + refund
# 40% of tickets resolved autonomously
LLMOps & Evaluation

Treat prompts and models like production code

You can't improve what you don't measure. We build evaluation suites, version prompts, and track quality, cost and latency in production — so every change is a known quantity, not a vibe.

  • Automated eval suites gating every prompt change
  • Cost, latency and quality tracked per request
  • Guardrails for safety, PII and prompt injection
eval — ci gate
$ devotica eval run prompt@v7
accuracy: 94.2% (baseline 91.8)
groundedness: 0.97
avg cost: $0.0021 / req
p95 latency: 1.3s
injection tests: 128/128 blocked
# gate passed · safe to promote
AI Strategy & Enablement

Bet on the use cases that pay off.

We help you separate AI theatre from real value — and build the foundation to capture it.

Use-case discovery

Workshops to find the handful of opportunities with real ROI and acceptable risk — and the data to back them.

Data readiness

An honest look at whether your data can support AI — and a plan to close the gaps if it can't yet.

Governance & risk

Policies for data use, model choice, safety and compliance — so AI scales without scaring legal.

Build vs. buy

Clear-eyed guidance on what to build, what to buy, and how to avoid lock-in as the model landscape shifts.

Team enablement

Reference architectures, patterns and hands-on upskilling so your engineers own what we build together.

Rapid prototyping

A working proof of value in weeks, on real data, so decisions are based on evidence — not a vendor deck.

40%
Support work automated with AI
94%
Answer accuracy on grounded RAG
2 weeks
From idea to a working prototype
0
Model-provider lock-in
AI project scoping

Turn the AI hype into something that ships.

Tell us what you want AI to do. We'll come back with the highest-value use case, an architecture, and a plan to get it into production.

Scope an AI project →