Tangensys • Plan, deliver, measure
LLM API integration
Select a model and integration pattern for the actual task. Quality, latency, cost and data handling should be tested together.
The work to plan together
A scope shaped around this requirement.
- Use-case designScope
- Integration buildReview
- Cost and safety controlsCheck
- Clear business factsConsistent details across the web
- Relevant pagesPlain answers to buyers’ questions
- Answer sourcesPages AI tools may draw on
- AI answerEach tool decides what to show; citations are never guaranteed
In short
What does LLM API integration involve?
LLM API integration connects models from providers such as OpenAI, Anthropic and Google to your website, apps and internal tools.
When LLM API integration is a useful fit
Product teams
Define the model-assisted action, data permissions and acceptable failure behaviour.
Businesses automating text tasks
Test output format, usage limits and the human review required for the task.
Problems worth addressing first
Use these situations to identify the work that deserves attention before expanding the scope.
- You want AI features but lack in-house skills
A practical example
A support system can request a short draft response, validate its format and let a staff member approve it. Unexpected output should trigger a fallback.
This is an illustrative scenario to explain the approach, not a claimed client project.
The work we can include
The final quote identifies the deliverables, quantities, approvals and exclusions. Select the work that serves your goal rather than assuming every item is required.
- Use-case design
- Integration build
- Cost and safety controls
- Testing
Where we usually begin
Provider terms, quotas, secret management and usage costs are reviewed. Model output is probabilistic and needs task-specific evaluation.
Practical detail
An output contract with a useful fallback
An illustrative application asks a model to summarise a support request into topic and summary fields. The rest of the application should not depend on receiving perfect free-form text.
| Part of the work | Example output | How to review it |
|---|---|---|
| Input boundary | Approved fields, length limits and permitted data | Unnecessary private data is excluded. |
| Output contract | topic, summary and needs_review fields | The application validates the returned structure and allowed values. |
| Failure route | Timeout, invalid response or provider error | The task goes to a defined retry or human queue. |
| Operating handover | Usage limits, cost visibility and evaluation cases | The owner can review failures and adjust the approved configuration. |
Provider availability and model behaviour can change. Keep a representative evaluation set and test material changes before releasing them into the business workflow.
Treat the model as one dependency in the application
An LLM API connection needs a defined input, output and supported use. The application should handle unavailable responses, unexpected content and provider changes. Calling a model successfully is only the beginning of the integration.
Agree which data may be sent and what should be retained. Prompts, retrieved sources and output validation affect the result. Cost controls need realistic use assumptions rather than one small demonstration request.
Test representative questions, invalid output and provider failure. Keep secrets outside public client code and define monitoring and fallback ownership. Model capabilities and terms are checked against the actual provider before implementation.
Free AI project review
Start with a clear scope and a practical next step.
Tell us what you want to improve and share the relevant website or project details. We will review the starting point, recommend a suitable scope and provide a custom quote.
The initial review is free. Delivery, third-party charges and ongoing support are quoted separately.
Process
How the work is delivered
Review the starting point
Review the current setup, examples of the problem and the information needed to agree the work.
Agree scope and sign-off
We start with real examples of the task and approved information. A small, clearly defined pilot tests usefulness, failure cases and the level of human oversight before wider rollout.
Deliver and review
Evaluation includes inaccurate answers, unavailable tools and uncertain requests. Provider charges, monitoring and updates are defined separately; the business keeps an accountable human owner.
Hand over the next steps
Review the agreed outputs together. Confirm what your team maintains and what needs a separate ongoing arrangement.
What progress should mean
Measures that match the work
Track useful-output rate, latency, usage costs and fallback frequency on representative inputs. Re-evaluate when the prompt or model changes.
A record of delivery
See what was completed, reviewed and still dependent on access or approvals. Project delivery and business results are reported separately.
Clear limits in the data
We explain what can be verified and where a result is only an estimate. Contact clicks, conversations and sales are different actions.
Scope and responsibilities
You keep ownership of your business accounts and approved deliverables under the agreed contract. Any third-party licences, subscriptions or usage fees are identified before you commit.
Can we change models later?
It may be possible, but outputs and supported features can differ. Keep the integration contract and evaluation set clear so an alternative can be reviewed. Do not assume two APIs are interchangeable without testing the application’s required behaviour.
Does structured output guarantee a correct answer?
No. It helps the application handle format. The content still needs evaluation against the actual task and approved information.
How are usage costs controlled?
Agree input limits, request limits, monitoring and the response to a exceeded budget or quota. Actual provider charges belong in the proposal.
Which model do you use?
We choose per use case, balancing quality and cost.
How is LLM API integration priced?
We quote against your goals, current setup and agreed scope. The quote separates delivery from third-party costs and ongoing support. You receive a custom quote within 2 working days.
How long does the work take?
The timeline follows the size of the agreed scope, access, content and approvals. We confirm milestones in the quote; a simple change and a full implementation need different schedules.
How will we communicate?
Your point of contact agrees a review rhythm with you. We use email, WhatsApp and scheduled calls, with decisions and scope changes recorded clearly.
What do you need to get started?
Share your current website or system, your main goal and examples of what is not working. We then agree the access and information needed for discovery.
Is everything included in the free review?
No. The free initial review helps define the next step. Detailed research, design, implementation and ongoing support are included only when stated in your quote.
Explore the related services
Let’s agree the right next step.
Tell us your goals and current setup. We’ll recommend a practical starting point and provide a custom quote.
Custom quote within 2 working days