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DevBatch

DevBatch Services

AI Engineering

LLM apps, RAG, evals, fine-tuning, agentic workflows. We build AI that ships to your users — not slide decks.

  • Production LLM apps, not demos
  • Eval coverage from day one
  • Models from Anthropic, OpenAI, open-source
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Who it's for

You probably need this if…

  • Product teams adding AI to a shipping product
  • Companies that tried AI features in 2023, hit reliability ceilings, and need to do it right
  • Founders building AI-native products from scratch
  • Teams that need someone who knows when fine-tuning beats prompting and vice versa

What's included

What you get

  • LLM application engineering (chat, search, summarization, extraction)
  • Retrieval-augmented generation (RAG) with real evaluation
  • Agent and tool-use workflows
  • Fine-tuning when it actually pays back over prompting
  • Evals — both offline benchmarks and production monitoring
  • Cost and latency optimization

Why most AI features still do not work in production

The hard part of AI engineering is not the model. It is everything around it — the retrieval layer that finds the right context, the eval harness that catches regressions, the prompt that holds up across edge cases, the fallback path when the model gets it wrong.

Demos hide all of that. Production cannot.

We build the parts of an AI feature that determine whether it ships and stays shipped. The model is the easy part.

What we do not do

We do not train foundation models. We do not chase generic agent frameworks for their own sake. We do not promise reliability we cannot prove with evals.

If your real problem is a search problem, or a workflow problem, or a data quality problem dressed up as an AI problem, we will tell you.

How it works

Three steps. No theater.

  1. 01

    Discovery

    30-minute call to understand what you want users to do, what your data looks like, and what you have already tried. We will tell you upfront if AI is the wrong tool.

  2. 02

    Prototype

    Two-week build of the riskiest part — usually the retrieval layer or the eval harness. You see whether this works on your real data before committing to a full build.

  3. 03

    Ship

    Production deploy with monitoring, evals on every release, and a runbook your team can own. We hand it over with the keys, not the dependency.

Tech stack

What we ship in

Models

  • Claude (Anthropic)
  • GPT (OpenAI)
  • Llama, Mistral (open-source)
  • Gemini (Google)

Frameworks

  • LangChain
  • LlamaIndex
  • Pydantic AI
  • Anthropic SDK
  • OpenAI SDK

Vector & retrieval

  • pgvector
  • Pinecone
  • Weaviate
  • Qdrant
  • ElasticSearch

Evals & observability

  • Braintrust
  • Langfuse
  • Helicone
  • custom eval harnesses

Infra

  • AWS Bedrock
  • Azure OpenAI
  • Modal
  • Cloudflare Workers AI

Pricing

Engagement models

Pick the structure that fits your situation. We'll talk through which one makes sense on the discovery call.

Two-week prototype, fixed bid

Dedicated team for full builds

T&M for ongoing eval and tuning work

Industries

Where we've shipped

  • SaaS
  • Fintech
  • Healthcare
  • Legal tech
  • Customer support
  • Sales and marketing platforms

Frequently asked

Questions we get a lot

How is this different from hiring an ML team?
Most teams asking for AI today do not need ML researchers. They need engineers who have shipped LLM features, know which patterns are reliable, and can write the eval harness that keeps the feature from regressing on the next model release. That is what we do.
Do you fine-tune or just prompt?
Both, depending on the problem. Fine-tuning costs more upfront and pays back when you have a stable, narrow task with enough labeled data. Most product features start with strong prompting plus retrieval, and we tell you honestly when fine-tuning would be wasted effort.
Can you work with our existing infra and model choices?
Yes. We work across Claude, GPT, Gemini, and the open-source families. If you are already on AWS Bedrock or Azure OpenAI, we work inside that. If you do not have a model preference yet, we will recommend one based on your task, latency budget, and cost ceiling.
What about evals?
Eval coverage is non-negotiable for us. Every feature we ship has an eval harness — offline benchmarks against a held-out set, plus production sampling. Without that you cannot tell when a model update breaks your feature, and you will eventually find out the hard way.

Ready to talk ai engineering?

30-minute discovery call. No spec sheet to fill out. We'll tell you upfront if we're not the right fit.

Book a discovery call