Pravija AI consulting
LLM · RAG · Agents · In production

Your LLM feature works in the demo. Does it hold up with real users?

Pravija helps SaaS and fintech teams make LLM, RAG and agent features accurate, fast and affordable. Start with a two-week reliability audit: you get a ranked fix list, not a slide deck. Specialists in legal and compliance AI.

Book a free 30-min call See the audit

Senior-architect led · 15+ years in data science and ML · Experience at Cisco and Yahoo · Masters in Machine Learning & AI

train loss val loss epoch 50 · early stop
Offers

Four ways to work together

Fixed scope and a fixed fee for each. Most teams start with the audit, because it shows exactly what to fix first.

Start here · 2 weeks

LLM reliability audit

We review your live LLM feature end to end: retrieval, prompts, evaluation, cost and latency. You receive a ranked list of what is failing, why, and how to fix it.

quality & hallucinationseval set & RAGAScost per querylatency
4–8 weeks

RAG & agent build

From prototype to production: hybrid search, reranking, tool-using agents, guardrails, monitoring and an evaluation harness your team keeps.

RAGagents & MCPfine-tuningobservability
2–4 weeks

Cost & latency optimization

Cut the inference bill and response time with model selection, caching, batching, prompt compression and smaller fine-tuned models, verified against your quality bar.

model routingcachingLoRA / QLoRAGPU inference
Monthly

Fractional AI architect

Ongoing architecture reviews, design decisions and hands-on guidance for your engineers, for teams that need senior AI judgment without a full-time hire.

design reviewsroadmapvendor & model choiceteam coaching
Who it's for

Sound familiar?

We work with engineering leaders at SaaS, fintech and enterprise-software companies whose LLM feature is built but not yet dependable.

"It makes things up."Answers sound confident but are wrong or not backed by your documents.
"The bill keeps growing."Cost per query is climbing and nobody can say which part is driving it.
"It's too slow."Users wait many seconds for a response and drop off.
"We can't tell if it's getting better."No evaluation set, so every change is a guess.
"The agent is unpredictable."Multi-step workflows fail in ways that are hard to reproduce.
"We need senior help, not a hire."The team is strong but has not shipped this kind of system before.
How we work

From first call to a fix list

Each stage ends with something you can evaluate, so you can stop, adjust or go further with real evidence.

  1. Intro call

    A free 30 minutes on your product, what is going wrong, and whether we can help.

    30 min

  2. Audit

    We build an evaluation set from your real traffic and measure quality, cost and latency.

    2 weeks

  3. Fix plan

    A ranked list of fixes with expected impact and effort, which your team can act on alone.

    end of audit

  4. Build or advise

    Optionally we implement the fixes or stay on as your architect. The system stays yours.

    optional
FAQ

Common questions

Not here? Email us and you will get a straight answer.

What do I need to start the audit?

Read access to your LLM feature's logs or a sample of real queries and answers, plus a short walkthrough of the architecture. We sign an NDA first if you want one.

How much does it cost?

Each offer has a fixed fee agreed up front, so there are no surprises. Send a few lines about your setup and you will get a quote within two working days.

Do you work with our data and models?

Yes. We work inside your cloud and tools, and we are model-agnostic across hosted APIs and open-source models.

Is my confidential or privileged data safe?

Yes. We work inside your cloud or on-premise environment, can use private or self-hosted models, never train on your data, and sign an NDA first. Documents do not leave your control.

Do you give legal advice?

No. Pravija builds and audits AI technology. It does not provide legal services, and we recommend that a qualified lawyer reviews any AI output used in legal work.

Who will I work with?

A senior practitioner with more than 15 years in data science and ML, who does the work directly. There is no hand-off to a junior team.

Is this only for big companies?

No. The sweet spot is a product team of roughly 50 to 1,000 people with an LLM feature already live or close to launch.

What happens after the audit?

You keep the report and the evaluation set. Many teams fix things themselves; others ask us to implement the changes or stay on as a fractional architect.

About
Founder-led
One senior practitioner, start to finish

Pravija is led by a data scientist and ML engineer with more than 15 years of experience building analytics and machine learning systems on large datasets, from data ingestion and feature engineering to model training, evaluation and production integration.

Our founder's recent work covers agentic AI platforms with multi-agent orchestration and MCP, and an enterprise RAG platform over millions of documents. You work directly with the person building your system, from the first data review to the production rollout.

Contact

Let's look at your LLM feature.

Book a free 30-minute call, or email a few lines about your product and what is going wrong. You will get a reply within two working days.

hello@pravija.ai