Hire LLM Developers

Retrieval-augmented generation (RAG) lets a model answer from your documents, product data, tickets, and records instead of relying on what it learned in training. The pipeline covers ingestion and cleanup, chunking, embeddings, and a choice of vector, keyword, or hybrid search based on how your users phrase their questions.

Since answer quality depends on retrieval quality, retrieval is tested against real questions, with re-ranking where it helps, source citations, and the access rights your systems already enforce. Indexes stay current as documents change.

Includes:

• Data ingestion and cleanup from documents, wikis, and databases
• Chunking and embedding strategy
• Vector, keyword, and hybrid search
• Re-ranking and source citations
• Permission-aware retrieval
• Index refresh as content changes

Fine-tuning is worth the investment when a task needs a consistent format, tone, or domain vocabulary, or when a smaller tuned model can take over high-volume work from an expensive one. Our developers check that first, because better prompts or retrieval often close the gap for less.

When tuning is justified, they prepare and label training data, run LoRA or full fine-tuning, and compare the result with the baseline on the same evaluation set. Datasets, training runs, and model versions are tracked, so results can be reproduced later.

Includes:

• Fine-tuning feasibility check
• Training data preparation and labeling
• LoRA, QLoRA, and full fine-tuning
• Evaluation against a baseline model
• Experiment tracking and model registry
• Deployment of tuned open-source models

Agents let a model take actions as well as produce text: look up data, call APIs, update records, and complete multi-step tasks. The design starts with limits: what an agent may do, which tools it can call, and where a person must approve an action first.

Agents are built with frameworks such as LangGraph or with direct tool calling, connected to internal systems through APIs or Model Context Protocol (MCP) servers, and logged step by step, so you can trace how each result was reached.

Includes:

• Agent workflow and permission design
• Tool and function calling
• MCP server development
• Multi-agent orchestration
• Human approval points for sensitive actions
• Step-level logging and tracing

Many teams need an LLM feature inside a product or workflow that already exists: a SaaS platform, a CRM, a help desk, an internal portal, or a mobile app. We add these features without rewriting what already works, connecting models to your data and services through APIs, webhooks, and event queues.

The work follows your architecture and coding standards, plans the interface for slow or failed responses, and respects the roles and permissions your system already enforces.

Includes:

• LLM features in web and mobile apps
• Integration with CRM, ERP, support, and document systems
• API, webhook, and event-driven connections
• Role- and permission-aware data access
• Interface patterns for AI responses and errors

Control is not a separate purchase. Every agent we build ships with defined access boundaries, approval rules, evaluation scenarios, fallback behavior, and activity records, so you can see exactly where a person stays in the loop.

Evaluation combines task-level quality checks with operational measures such as completion rate, processing time, escalation rate, latency, and cost where relevant.

Includes:

• Data and action boundaries with role-based access
• Human approval workflows for sensitive actions
• Evaluation datasets and scenarios
• Failure and escalation handling
• Audit and review requirements

LLM output varies from one request to the next, so quality needs regular measurement. Evaluation sets built from real examples score accuracy, relevance, and policy compliance, and the checks rerun whenever prompts, data, or models change.

Security work covers prompt injection and data leaks, input and output filtering, keeping personal data out of prompts and logs, and routing uncertain or high-risk answers to a person for review.

Includes:

• Evaluation datasets and scoring methods
• Hallucination and accuracy testing
• Regression checks for prompt and model changes
• Prompt injection and jailbreak protection
• Input and output filtering, PII redaction
• Human review rules for high-risk output

A working LLM feature can still be too slow, too expensive, or too inconsistent to scale. If you need an LLM optimization partner for a system that is already live, our engineers start by profiling where time and money go: tokens, prompt length, retrieval calls, model choice, and infrastructure. Caching, prompt optimization, routing simple requests to smaller models, batching, and quantization for self-hosted models then bring cost and latency down.

LLMOps keeps those gains from slipping: CI/CD for prompts and models, monitoring of quality, latency, and spend, and alerts that catch drift or cost spikes early enough to roll back a bad release.

Includes:

• Cost and latency profiling
• Prompt optimization and caching
• Model routing and fallback strategies
• Quantization and self-hosted inference tuning
• CI/CD for prompts and models
• Quality, latency, and spend monitoring