AI & LLM
Application Development
You want an AI feature, an agent, or a full product built -- not a permanent bet on one company's model. We build the application around your data and your workflow, and keep the provider underneath it swappable, so a price change, a deprecation, or a better model elsewhere never means starting over.
Use Your Preferred AI Provider
We build against the provider that fits the project, your budget, and your data requirements -- not the one we happen to prefer. If you already pay for a provider, we build on the account you have.
Anthropic Claude
The model family this site itself is built with. Strong at careful reasoning, long documents, and following detailed instructions.
OpenAI
GPT models and the broader OpenAI platform, including its agent and assistant tooling.
Google Gemini
Google's model family, including its native multimodal and long-context capabilities.
AWS Bedrock
A managed way to run several model families inside your own AWS account, with the billing and access controls you already use.
Open-weight models
Llama, Mistral, and other openly licensed models, run through a hosted API or on infrastructure you control.
Whatever you already use
Already have an enterprise agreement or an approved vendor? We build against the account and contract you already have.
Provider-Independent Architecture
The provider sits behind one layer in the application, not threaded through it. Prompts, tool definitions, and business logic live in your codebase; the provider is a swappable dependency, not the foundation everything else is built on.
What this protects you from
A provider raising prices, deprecating a model, changing its terms, or having an outage does not strand your application. You can move to a different model, or a different provider entirely, without starting over.
Private & Local LLM Solutions
Some data should not leave your own environment, and some clients are not permitted to send it to a third-party API at all. For that work, we deploy models privately -- inside your own cloud account or on your own infrastructure -- so nothing goes to a provider you do not control.
The fastest way to start, and the right choice for most projects: your application sends requests to the provider's API and gets a response back.
For sensitive data or strict compliance requirements: the model runs inside your own AWS account or on hardware you control, and nothing is sent to a third party.
- Data leaves your environment to a third-party API
- Model runs inside your own environment
AI Agents & Automation
An agent is a model that can take more than one step: it can call tools, read from and write to your systems, and decide what to do next based on what it finds -- with the guardrails and oversight that make that safe to run in your business.
Tool and function calling
The model can call the specific functions, APIs, and internal tools it needs to complete a task, not just generate text about it.
Multi-step task execution
Agents that plan a sequence of steps, execute them, and adjust when a step does not go as expected.
Connected to your systems
Agents that read from and write to the systems you already run, working inside the access you grant rather than a sandbox.
Human oversight and guardrails
Defined limits on what an agent can do without approval, and a clear path for a person to review or intervene.
Scheduled and triggered runs
Agents that run on a schedule, in response to an event, or on demand, instead of only inside a chat window.
Monitoring and logging
A record of what an agent did and why, so its actions can be reviewed and its behavior improved over time.
LLM Startup & MVP Development
Building a new AI product is different from adding AI to an existing one: the risk is not knowing yet whether the idea holds up, so the first job is finding that out cheaply.
Validate the idea
We help you decide what the smallest version of the product needs to prove, before committing to a full build.
Build a working prototype
A real, working version -- not a mockup -- built fast enough to put in front of actual users.
Test with real users
Feedback from real use, not assumptions, is what tells you whether to keep going, change direction, or stop.
Harden for scale
Once the idea is proven, we bring the prototype's architecture up to the reliability, cost control, and provider independence a real product needs.
Where AI Work Meets Our Other Technology Services
An AI feature rarely stands alone. It usually needs to read from or write to your existing systems, and the infrastructure it runs on needs to be built properly.
System Integration
Connecting the AI application to the CRM, database, or internal tools it needs to read from and write to.
Learn moreData & Reporting
Turning what an AI application produces into dashboards and reports people actually use.
Learn moreCloud Architecture & AWS
Hosting, scaling, and securing the infrastructure an AI application and its private models run on.
Learn moreCustom Software Development
The rest of the application around the AI feature -- the parts that are not the model at all.
Learn moreWhat Is and Is Not Included
An AI and LLM engagement covers the application, agent, or integration we agreed on. These boundaries come from our Terms of Service and apply to every technology engagement.
Included in a scoped build
- Discovery, provider evaluation, and a written scope
- Building the application, agent, or integration and its provider-abstraction layer
- Working within the access you provide to the systems involved
- Connecting to the AI provider or private/local model you choose
- Testing against real use cases, with credential turnover and instructions
- Documentation covering how to add, remove, or swap a provider later
Not assumed unless agreed in writing
- Any guaranteed model accuracy, output quality, or behavior
- Training or fine-tuning a new foundation model from scratch
- Ongoing monitoring, maintenance, or support after handoff
- Legal, tax, accounting, privacy-law, or regulatory advice
- Compliance certification or content-moderation guarantees for model output
- Third-party model, API, token, or hosting charges
Which provider or providers are in scope is part of the written agreement, including whether the build supports one provider or several. Adding support for an additional provider after the build is a separate, quoted change.
How AI & LLM Development Is Priced
You get a real number before work begins. Note that model and token charges are an ongoing cost, not a one-off.
| Fee | When it applies | Amount |
|---|---|---|
| Base service fee | Covers the accepted scope for the application, agent, or integration as written. | Quoted per project |
| Prototype / MVP engagement | A scoped, time-boxed build to validate an idea before committing to a full build. | Quoted per project |
| Hourly labor | Approved implementation, troubleshooting, or revision work beyond the base scope. | $65.00 / hour |
| Rush handling | Expedited turnaround or priority scheduling, when requested and accepted. | $150.00 flat |
| Third-party costs | Model, token, API, and hosting charges from your chosen provider, which continue for as long as it runs. | Passed through |
Fees are due upon invoice, and payment may be required before final launch, credential turnover, or handoff. If we find the actual scope differs materially from what was described, we stop and quote the revised number before doing the additional work.
You own and remain responsible for the account, billing relationship, and payment method with whichever AI provider is used, whether it is an account you already have or one set up in your name during the engagement. Usage-based charges -- tokens, API calls, and compute -- are billed by that provider directly and are not part of our fees. Pricing, availability, rate limits, and model behavior are set by the provider and may change without notice.
Data you send to a third-party AI provider is subject to that provider's own terms and data-handling policies, which we do not control; you remain responsible for confirming that sending your data to your chosen provider is permitted under your own obligations. AI-generated output can be inaccurate, incomplete, or unsuitable for a given use, and should be reviewed before it is relied upon, published, or acted on. We do not independently verify the accuracy of generated output.
You agree to review deliverables promptly. Unless we agree to a different acceptance method in writing, work is deemed accepted on the earliest of: your written approval; your use of the delivered work in production or public release; or five (5) business days after delivery without a written, reasonably specific objection tied to the agreed scope.
T&C Group Holdings, LLC is not a law firm and does not provide legal advice. Technology services are provided for implementation, support, troubleshooting, and general business-use guidance only. AI and language models are probabilistic and will produce incorrect, incomplete, or unexpected output; we do not guarantee any accuracy rate, error-free output, uptime, compatibility with every third-party service or provider, or any particular business or technical outcome. Human review reduces that risk but does not eliminate it, and final responsibility for what is done with AI-generated output remains yours. Ongoing maintenance, monitoring, and support are outside a standard engagement unless separately accepted in writing. This page is a summary for convenience; our Terms of Service govern.
Full terms: Terms of Service
What Would You Build If the Model Never Locked You In?
Tell us what you want the application or agent to do, and which provider -- if any -- you already use. We will come back with a scope, a number, and an honest read on what a first working version looks like.