Generative AI development services
Generative AI development: retrieval-augmented generation (RAG) over your own documents, AI agents, chatbots and assistants, LLM features inside your product, document processing, fine-tuning and evaluation of language models, and voice agents, with dedicated AI developers for your team or a generative AI project delivered end to end.
What we build with Generative AI
Retrieval-augmented generation
Answers drawn from your documents, wikis and tickets through hybrid vector and keyword search with re-ranking, in Azure AI Search, PostgreSQL with pgvector, Elasticsearch, Qdrant or Pinecone, with sources cited and each user’s document permissions applied at retrieval.
AI agents
Agents that look up records, draft replies, update tickets and run multi-step workflows through your APIs and Model Context Protocol servers, built with the OpenAI Agents SDK, LangGraph, Microsoft Agent Framework or Pydantic AI, with scoped credentials and a person’s approval before anything changes data or spends money.
Chatbots and assistants
Customer support chat on your website, in your app or on WhatsApp, and internal assistants in Microsoft Teams or Slack, with streamed responses, conversation history and a hand-off to a person when the bot cannot help.
LLM features in existing products
Summaries, drafting, classification, semantic search and in-product copilots added to a .NET, Java, Python or Node.js application, with the model provider behind one interface and the cost and latency of every request logged.
Document processing
Invoices, contracts, identity documents and forms turned into structured data by multimodal models with schema-constrained output, validated like any other input, and low-confidence fields routed to a person for review.
LLM evaluation and guardrails
An evaluation set built from real questions and scored with promptfoo, Ragas or DeepEval on every change to a prompt, model or retrieval setting, with traces in Langfuse, prompt-injection tests and personal data redacted where the model does not need it.
Fine-tuning and open-weight models
Llama, Mistral, Qwen, Gemma and gpt-oss models fine-tuned with LoRA in Hugging Face TRL or Unsloth, or hosted models tuned through Microsoft Foundry or Gemini Enterprise Agent Platform (the successor to Vertex AI), and open-weight models served with vLLM in your own cloud when data must not leave it.
Voice agents
Phone and in-app voice assistants that answer questions, take bookings and route calls, built on speech-to-speech models through the OpenAI Realtime API and the Gemini Live API or on speech recognition, a language model and text-to-speech, with LiveKit or Twilio carrying the audio.
Image and content generation
Product images, ad variations, catalogue descriptions and localised marketing copy generated to your brand rules by language models and by image models such as FLUX, GPT Image and Google’s Nano Banana, with a review step before anything is published.
Hire AI developers
Dedicated AI developers
AI developers who join your team full time, work in your repositories, tracker and meetings, and report to your lead. You interview them; we carry the Ukrainian contract, payroll, invoicing and leave.
Generative AI project delivery
A team that takes the Generative AI project from scope to release: estimate, build, tests and deployment, with a technical lead on our side who owns the plan and the quality.
Generative AI support and take-overs
An existing Generative AI system taken over from another team or kept running: a read-only review and a written list of risks first, then fixes and new features in order of impact.
The dedicated team page explains how specialists join your team, and the outsourcing page covers project delivery, take-overs and how we charge.
Who works on your Generative AI project
AI developers
Python, LLM APIs, agents and retrieval
Machine learning engineers
Fine-tuning and self-hosted models
.NET and Node.js engineers
AI features inside existing applications
Front-end engineers
Chat interfaces and streamed responses
QA engineers
Evaluation sets and end-to-end tests
How we build LLM applications
Evaluation comes before features. We agree a set of real questions and expected answers with you, score every change to a prompt, model or retrieval setting against it in the pipeline, and log the cost and latency of each request, so quality and spend are measured rather than judged from a demo.
Model output and retrieved text are treated as untrusted input. Agents get the narrowest credentials that do the job, actions that change data or spend money wait for a person’s approval, keys come from a vault, and every agent action is logged so it can be traced afterwards.
Other data and AI services we provide
AI and machine learning
AI and machine learning development services: forecasting, fraud detection, recommendation systems, computer vision, NLP and speech recognition, and MLOps, with dedicated machine learning engineers or project delivery.
Data engineering
Data engineering services: ETL pipelines, Spark, dbt and streaming, warehouses and lakehouses on Snowflake, Databricks, BigQuery, Redshift and Microsoft Fabric, with dedicated data engineers or project delivery.
Data analytics and Power BI
Data analytics and Power BI services: dashboards in Power BI, Tableau and Looker, product analytics, A/B testing, financial and marketing reporting, with dedicated data analysts and BI developers or project delivery.
Database development
Database development and administration services: schema design, query tuning, migrations and backups on SQL Server, PostgreSQL, MySQL, Oracle and MongoDB, with dedicated database developers and DBAs or project delivery.
Computer vision development
Computer vision development services: object detection, visual inspection, OCR, video analytics, medical imaging and models on edge devices, with dedicated computer vision engineers or project delivery.
Data annotation
Data annotation and labelling services for AI: image, video, text, audio and LiDAR labelling, RLHF and evaluation data, with quality control on every batch, from dedicated data annotators or project delivery.
MLOps
MLOps services and consulting: ML platforms, CI/CD for models, model registries, serving and autoscaling, GPU infrastructure, drift monitoring and LLMOps, with dedicated MLOps engineers or project delivery.
Databricks development
Databricks development and consulting: lakehouse set-up, Lakeflow pipelines, Unity Catalog, migrations, AI/BI, machine learning and cost control, with dedicated Databricks engineers or project delivery.
Snowflake development
Snowflake development and consulting: warehouse design, Snowpipe and Openflow ingestion, dbt, migrations, Cortex AI, security and cost control, with dedicated Snowflake developers or project delivery.
Kafka and event streaming
Apache Kafka development and consulting: event-driven architecture, Kafka Connect and Debezium, Flink and Kafka Streams, Confluent Cloud, MSK and Event Hubs, with dedicated Kafka engineers or project delivery.
Workflow automation
Workflow and AI automation services: n8n, Make, Zapier and Power Automate flows, AI agents in workflows, CRM, finance and document automations, with dedicated automation developers or project delivery.
Questions about Generative AI development
Which industries do your AI developers work in?
AI products themselves, from copilots to agent platforms; fintech, for customer support, KYC document checks and compliance search; e-commerce and retail, for shopping assistants, product search and catalogue content; healthcare, for clinical documentation and patient communication; advertising and marketing, for ad copy and creative variations at scale; and enterprise SaaS, for assistants and search inside the product.
Will our data be used to train someone else’s model?
No. We use business APIs whose terms exclude training on your inputs, from OpenAI, Anthropic or Google directly or through Azure and Amazon Bedrock, or open-weight models hosted in your own cloud when the data must not leave it.
Which model do you use?
The least expensive one that passes your evaluation set, from OpenAI, Anthropic, Google or Mistral, or an open-weight model you host. The provider sits behind one interface in the code, so moving to a newer or cheaper model is a configuration change and a re-run of the evaluation, not a rewrite.
How do you stop a chatbot from making things up?
By grounding answers in retrieved sources and showing the citations, instructing the model to say when the sources do not answer the question, and keeping unanswerable questions in the evaluation set. That reduces invented answers and measures what is left; no current model removes them entirely.
How quickly can AI developers start?
When the right AI developer is available, the start is gated only by your interview and the NDA and IP assignment. Otherwise we run a search, which typically produces candidate profiles within two to three weeks, and nobody starts until you have said yes.
How do we hire AI developers through BigTree108?
Tell us the work, the seniority and the hours you need. We propose one or two people with their profiles, you interview them the way you would interview your own hire, and you sign one agreement with BIG TREE 108 LLC and receive one invoice a month.
Who owns the work they produce?
You do. Every specialist has a signed contract with BigTree108 that assigns all work product to the company, and our agreement with you assigns it onward. Code, designs and documents are delivered into your own repositories and tools, not kept where only we can change them.
Planning an LLM feature or an AI agent?
Tell us what it should answer or do and where the data lives. You get an answer within one business day: a plan, candidate profiles, or a recommendation on the model and the architecture.