Service · 01

AI Development.

Custom LLM applications, retrieval-augmented systems and fine-tuned models — built for production, not for demos. Evaluation harnesses, observability and unit-cost controls included by default.

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Overview

What “AI Development” means at Devmint.

AI Development at Devmint means the design and engineering of custom LLM applications that ship to production with the same operational discipline as any other system you depend on. We build retrieval pipelines, fine-tuned models, multi-step reasoning chains and the supporting evaluation, observability and cost-control layers that determine whether the system actually works on a Tuesday afternoon — not just in a demo.

Most LLM systems we replace were built fast, then quietly stopped working when traffic, data quality or model versions shifted. Devmint's engagements start with the assumption that the model will change three times during the build, the data will be messier than promised, and the unit economics matter as much as the user experience.

What you get

Deliverables.

Custom LLM application architecture
Retrieval / RAG pipeline + vector store
Eval harness with regression tests
Cost, latency and safety guardrails
Observability dashboard + runbook

How it ships

The shape.

A typical AI Development engagement runs eight weeks against three checkpoints. Week one is a technical spike and the eval contract — we define how we'll measure 'good enough' before we write any prompt. Weeks two through six are weekly production releases behind feature flags, with live demo and decisions at the end of each week. Weeks seven and eight harden the system, tune cost, document, and hand off — though most clients renew straight into operate-and-improve.

Investment

How we price.

Devmint engagements are scoped as fixed proposals against measurable outcomes — not hours. After a 30-minute discovery call, we send a written proposal with timeline, deliverables, eval targets and a single fixed fee. No procurement maze, no T&M creep. Smaller pilots and larger outcome-based contracts are scoped the same way — tell us what you're shipping and we'll come back with a number.

Tech stack

What we reach for.

Defaults — OpenAI, Anthropic, Mistral and open-weights via Together / Fireworks; Pinecone, pgvector or Weaviate for retrieval; LangGraph or custom orchestration; Langfuse, OpenInference and OpenTelemetry for observability; Next.js, Python and Go for application code; Postgres, Redis, Cloudflare and AWS for infra. Every choice gets defended in writing against measurable trade-offs.

FAQ

Common questions.

Do you fine-tune models?

Yes — when it improves task accuracy at lower cost than a stronger base model with better prompting. Devmint defaults to prompt-engineering and retrieval first, and reaches for fine-tuning when evals show a measurable gap that prompting cannot close.

Which models do you recommend?

It depends on task, latency and unit cost. Devmint typically routes between two or three models per project — a cheap fast model for the 80% of traffic, a stronger model for harder cases, and an offline model for batch work. We benchmark them against your data before committing.

How do you handle hallucinations?

With layered defenses: retrieval grounding, structured outputs with schema validation, programmatic guardrails on critical fields, an eval harness that catches regressions before release, and a human-review surface for high-stakes decisions. We can never eliminate hallucinations — we measure, bound and contain them.