LLM & generative applications
Copilots, internal tools, and custom GPT-style assistants grounded in your own data and tone — for drafting, summarization, and everyday knowledge work.
AI WILL GET IT DONE FOR YOU!
Your data has all the answers.
We help SMB founders and operations leaders unify their data, get answers from it, and automate the work that slows their business down.
Built for teams with something real to build on.
A good fit
- You hold real domain knowledge — the hard-won expertise that generic models do not have.
- You run live operational systems, tools, and data an AI could actually act on.
- You have a team that can validate outputs and own the results in production.
- You want AI that takes action, not just a demo that answers questions.
Not a fit
- You are pre-product and want a generic chatbot with nothing real to integrate.
- You need a throwaway prototype with no path to production or evaluation.
- You expect AI to replace judgment entirely, with no one to validate outputs.
- You are shopping for the cheapest possible API wrapper.
Six capability pillars across the full spectrum of applied AI.
Retrieval & knowledge systems (RAG)
Domain-aligned document parsing, ingestion pipelines, vector search, and evaluation so answers stay accurate as your source data changes.
Agentic systems & MCP integration
Task-executing agents, multi-agent orchestration, and Model Context Protocol servers and clients that let models safely call your real tools, APIs, and internal systems.
Voice AI
Natural, low-latency voice agents with speech-to-text and text-to-speech pipelines, plus call infrastructure — routing, escalation, monitoring — at real call volume.
Predictive ML & intelligent automation
Forecasting, classification, anomaly detection, and recommendation engines, plus AI-native workflow automation that replaces slow manual review.
AI infrastructure, security & strategy
MLOps and model deployment, evaluation and monitoring pipelines, guardrails and governance, and technical AI strategy and audits for deciding what to build first.
The honest limits of the obvious alternatives.
Just use ChatGPT or Claude directly
They have no grounding in your private data, no access to your systems, and no evaluation — so they guess confidently and no one owns the errors.
Build it in-house
A capable team still spends months learning retrieval quality, agent reliability, and eval harnesses before shipping — time most roadmaps cannot spare.
An off-the-shelf RAG or automation platform
Generic connectors and Zapier/Make flows break on real document structure, edge cases, and access control the moment volume and complexity rise.
A voice API alone (Twilio / Vapi)
You get a phone line and a model, but not the routing, escalation, grounding, and monitoring that make a voice agent trustworthy at real call volume.
Treat every problem as a RAG problem
Some tasks need an agent that acts, a predictive model, or plain automation — forcing retrieval everywhere adds latency and hides the real fix.
Discover, architect, build, operate.
01
Discover
We map your domain, systems, and constraints to find the one use case where AI creates real leverage first.
02
Architect
We design the retrieval, agent, voice, or model architecture — plus the evaluation that proves it works.
03
Build
We ship a production system inside your environment, integrated with the real tools and data it needs to act.
04
Operate
We monitor quality, tighten guardrails, and iterate as your data and requirements change — we stay past launch.
Data & security
Your data stays yours.
Serious AI has to live where your compliance and access controls already are. We engineer inside your environment and around your real obligations — never on a black-box platform you have to trust blindly.
Inside your cloud
We build within your own cloud accounts and access controls — not a shared multi-tenant platform your data has to leave.
Guardrails by default
Least-privilege tool access, auditable actions, and monitoring so an agent can only ever do what you have approved.
Built for real compliance
We design around SOC 2, HIPAA, and data-residency requirements from day one, rather than retrofitting them later.
Selected work
Shipped for real teams.
A selection of production systems we've built across healthcare, tax, trade finance, sustainability, sales and operations.
A virtual AI CEO grounded in Slack, email, meeting transcripts and Limitless AI — surfacing blockers, priorities and weekly summaries on demand.
4
live data sources unified into one assistant
Automated mapping of Voluntary Sustainability Standards against CS3D and EUDR regulations, with alignment scoring and cited evidence.
Weeks → mins
assessment time cut from weeks of expert review
An AI doctor’s assistant analyzing patient patterns and surfacing Portuguese-language, evidence-based treatment support in real time.
500K+
practitioners reached at launch
A code-first analytics engine reasoning over millions of healthcare records — precise cohorts, charts and case studies beyond RAG’s limits.
100%
data fidelity across millions of records
A fine-tuned Whisper model transcribing and summarizing bilingual Hebrew–English sermons with cultural nuance intact.
500%
reduction in word error rate
A RAG system querying 2,000+ SOP and business-standard PDFs with context-aware, source-cited answers.
Hours → secs
search time across thousands of documents
AI compares Letter of Credit instructions against customer drafts, flagging discrepancies, missing fields and multi-condition clauses.
35–40s
per review, down from hours of manual work
An on-prem open-source vision model extracting handwritten data from confidential tax documents at 98%+ accuracy.
14,000%+
increase in document processing speed
An AI sales coach scaling a client’s proprietary training across interns, team leads and senior management.
250+
sales reps onboarded at scale
Who we are
A small, senior studio — not a marketplace or a wrapper.
llmragai is a focused team of senior AI engineers. You work directly with the people who architect and write the system — not a rotating bench of subcontractors, and not a thin layer over someone else's API. We take a small number of engagements seriously and stay through deployment and operation.
How engagements start
- 01
Discovery call
A working conversation to pressure-test the idea and find the right first use case.
- 02
Fixed-scope pilot
One real use case, shipped to production with clear success criteria — before we expand into a broader roadmap.
Start with clarity
Turn your data into your unfair advantage.
Tell us what is slowing your team down. We'll reply within one business day with a practical next step.
- Bring one real use case — we scope it with you.
- Honest read on whether AI is even the right tool.
- A concrete plan to ship it into production.