AI Agents & Data
Generative AI development services that survive contact with real users
We build LLM applications, document assistants and AI agents with the testing, guardrails and cost controls that a demo skips.
- 01RAG over company documents
- 02AI agents and workflow automation
- 03Copilots inside your apps
- 04Document extraction
- 05Evaluation and testing
- 06Guardrails and security
- 07Cost and latency control
- 08MCP servers and integrations
At a glance
Generative AI development is building software on large language models, such as assistants that answer from your documents, copilots inside your apps and AI agents that process work like invoices. It's for companies ready to move past experiments into production. Tag4 Technology Solutions builds these with evaluation sets, guardrails and cost controls, on OpenAI, Anthropic Claude, Azure OpenAI or open-weight models.
Updated
- Best for
- Companies moving from AI experiments to assistants and agents in production
- Typical timeline
- 6–10 weeks for a RAG pilot, 3–6 months for customer-facing products
- Typical running cost
- Under $500 a month for internal tools, several thousand at high volume
- Core technologies
- OpenAI, Anthropic Claude, Azure OpenAI, Amazon Bedrock, LangGraph, pgvector
- What you get
- A tested app with evals, citations, guardrails and per-feature cost tracking
- Data handling
- Deployed in your own cloud account, with your data excluded from model training
Building a chatbot demo now takes an afternoon. Building one your staff trusts with a customer contract takes considerably more. Our generative AI development services are for companies that have moved past experimenting and want something in production: a copilot inside their CRM, an assistant that answers from their policy manuals, or an agent that processes invoices and only asks a person when something looks off.
The common failures are predictable. Retrieval pulls the wrong chunk of a document, so the model answers confidently from the wrong page. There's no evaluation set, so every prompt change is a guess and regressions show up in front of customers. Agents get too many tools and too much freedom, then loop or take actions nobody approved. Then the first monthly invoice arrives and it's five times the estimate because every request sends 40 pages of context to the largest model available.
Our work starts with the questions users actually ask. We collect a few hundred real examples and turn them into an evaluation set before writing much code, so every change to prompts, chunking or model choice gets scored. RAG pipelines use hybrid search (keywords plus vectors) with re-ranking and citations back to the source paragraph. Agents get narrow, permissioned tools and human approval on anything that writes to your systems. We stay model-agnostic, working with OpenAI, Anthropic Claude, Azure OpenAI and open-weight models such as Llama, Mistral and Qwen, and route each task to the cheapest model that passes the evals.
A focused internal assistant or RAG pilot usually takes 6–10 weeks. A customer-facing product or a multi-step agent with integrations is more often 3–6 months. Running costs vary widely, from a few hundred dollars a month for an internal tool to much more at consumer scale, and we estimate them from real token counts before you commit.
What we do
Generative AI Development services
RAG over company documents
Assistants that answer from your contracts, SOPs, tickets and wikis, with citations to the exact source and respect for who is allowed to see which file.
AI agents and workflow automation
Agents that read an email, look up the order, draft the reply and wait for approval. Tool access is scoped, logged and reversible.
Copilots inside your apps
Summaries, drafting and natural-language search added to your existing CRM, ERP or customer portal, instead of a separate chat window nobody opens.
Document extraction
Pulling structured fields from invoices, claims, bills of lading and forms into your database, with confidence scores and a review queue for edge cases.
Evaluation and testing
Test sets built from real questions, automated scoring, and LLM-as-judge checks calibrated against human grading, run on every release.
Guardrails and security
Prompt injection defenses, PII redaction, output validation and audit logs. We treat every document and web page the model reads as untrusted input.
Cost and latency control
Model routing, prompt caching, smaller context windows and batch processing where speed is not critical. Spend is tracked per feature and per customer.
MCP servers and integrations
Model Context Protocol servers that give AI tools controlled access to your internal systems, so one integration works across multiple assistants.
How we work
What you can expect from us
Evals before features
We agree on how answers will be scored before building. That turns debates about whether the bot is good into a number you can track.
No single-vendor bet
Models improve and prices drop every few months. We keep the provider behind a thin interface so switching models is a config change plus an eval run.
Humans approve the risky steps
Anything that sends money, emails a customer or changes a record goes through a person until the logs show the agent can be trusted with it.
Your data stays in your tenancy
We deploy in your Azure, AWS or Google Cloud account using enterprise API terms that exclude your data from model training, or self-host open-weight models when required.
Tools and platforms
- OpenAI API
- Anthropic Claude
- Azure OpenAI
- Amazon Bedrock
- Llama, Mistral and Qwen open-weight models
- vLLM
- LangGraph
- LlamaIndex
- Model Context Protocol (MCP)
- pgvector
- Azure AI Search
- Langfuse
- promptfoo
Process
From first call to production
Discovery
A few working sessions to pin down goals, users, constraints and what "done" means. You get a written scope and estimate.
Design
Architecture, data model and clickable UI before heavy coding starts, so surprises show up early and cheaply.
Build
Two-week sprints with a demo at the end of each one. You see working software, not status reports.
Launch
Automated tests, staged rollout, monitoring and a rollback plan. Go-live should be boring.
Support
Bug fixes, updates and new features on a monthly plan, or a clean handover to your team.
FAQ
Generative AI Development: common questions
Will our data be used to train OpenAI or Anthropic models?
Not under their business API terms, which exclude customer data from training by default. For stricter needs we use Azure OpenAI or Amazon Bedrock inside your own cloud account, or host an open-weight model on infrastructure you control. We'll document where every piece of data goes.
How do you stop the AI from making things up?
You can't eliminate it entirely, but you can make it rare and visible. We ground answers in retrieved documents, require citations, and have the system say it doesn't know when retrieval comes back weak. Evals track the hallucination rate so you can see whether it's improving.
Which model should we use?
It depends on the task, and the answer changes often. We test two or three candidates against your evaluation set and compare accuracy, speed and cost per request. Frequently a smaller model handles most traffic, with a larger one reserved for the hard cases.
Should we fine-tune a model or use RAG?
Start with RAG for anything that depends on facts in your documents, because it stays current when documents change and shows its sources. Fine-tuning helps with consistent format, tone or narrow classification tasks. Most projects never need it.
How much does it cost to run a generative AI app each month?
It ranges from under $500 a month for an internal assistant with modest usage to several thousand for high-volume customer tools. The biggest drivers are how much context each request sends and which model handles it. We estimate costs from a sample of real traffic before launch.
Can the assistant respect our existing file permissions?
Yes. We carry permissions from SharePoint, Google Drive or your app through to retrieval, so a user only gets answers drawn from documents they could already open. This has to be designed in early, which is why we ask about it in the first meeting.
Related
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Learn moreData Engineering
Pipelines, warehouses and lakehouses on Snowflake, Databricks or Fabric, modeled with dbt and monitored.
Learn moreCustom Software Development
Software built around your processes, from small internal tools to full platforms, with code you own outright.
Learn moreIndustries we build for
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