LLM Integration Services
An API call can prove a model responds. It does not prove the feature returns valid output, stays within budget, handles provider failure, or knows when to stop. RaftLabs integrates language models into existing products with structured outputs, tool permissions, evaluation, fallbacks, monitoring, and a release path tied to one measurable workflow.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Evidence and scope
6 to 10 weeks
First feature
One workflow, one evaluation set, and production controls.
$15K
Starting scope
Model integration, validation, monitoring, and handover.
12 weeks
Delivered AI product
Perceptional moved from prototype to live platform.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does the model feature work in a demo but fail on real inputs, latency, cost, or inconsistent output?
Can the model call tools or write data without a clear permission boundary and human approval point?
Plain answer
LLM integration connects a language model to an existing product, data source, or workflow with controls around its output. RaftLabs develops structured outputs, tool permissions, evaluations, fallbacks, and monitoring. A focused first feature starts at $15,000 and usually takes 6 to 10 weeks.
The demo had clean inputs and one developer watching it. Production added long requests, missing fields, prompt injection, provider timeouts, and users who clicked twice because the first response looked stuck.
The model was never the whole feature. The integration around it decides what the model may see, which tools it may request, what output the product accepts, and what happens when the provider fails.
Delivered LLM proof
A hosted assistant or direct API call is better when the task has no proprietary data, tool access, or operating consequence.
A prototype already shows value, but real inputs expose quality, latency, cost, or reliability gaps.
The model must read private context, return validated data, or call an existing business tool.
A product owner can define acceptable output and the cases that require human review.
A single prompt in a hosted assistant already solves the job.
The request is a demo with no owner, user workflow, or release threshold.
The model would act on sensitive systems without a clear permission and approval design.
Scope
| Prompt and tools | RAG or fine-tuning | |
|---|---|---|
| Prompting fits | The model knows enough and needs instruction, format, or tool access | Private knowledge or repeatable behaviour remains missing |
| RAG fits | Context already fits safely in the request | Answers must use changing private sources and show evidence |
| Fine-tuning fits | Prompt examples reach the required consistency | A narrow task or style needs learned repeatability at scale |
| Update path | Change prompt, schema, or tool contract | Re-index knowledge or retrain and re-evaluate |
| First decision | Prove the workflow with the simplest model call | Add complexity only when evaluation shows the gap |
For answers grounded in company documents, the RAG development service owns retrieval and citation quality. Net-new AI products belong under generative AI development.
How it works
The release threshold is agreed before production code expands.
Choose one workflow, collect representative and adversarial inputs, and agree quality, latency, cost, permission, and escalation thresholds.
Test prompting, retrieval or tools, structured output, and provider behaviour against the evaluation set. Record the baseline rather than judging a few good responses.
Add authentication, permissions, validation, retries, fallbacks, tracing, cost limits, and usable states for slow, uncertain, or failed output.
Roll out to a bounded user group, inspect failures, compare prompt and model versions, and expand only after the feature clears its threshold.
RaftLabs developed Perceptional, a conversational AI interview platform, using Anthropic Claude through AWS Bedrock. The founder received a working conversation prototype in the first week. The full platform went live in 12 weeks and produced a structured summary within 48 hours of an interview ending.
Those are records from one product. They do not promise the same schedule or output quality for a different model, workflow, or data set.
Scope and price
Start with one workflow, one evaluation set, and the controls needed to release it inside software you already run.
Ongoing model and hosting charges are estimated from expected usage and remain separate from the fixed development phase.
Starting investment
Starts at $15,000
A focused first feature usually takes 6 to 10 weeks. Retrieval, tool count, sensitive data, and traffic move the estimate most.
Measured before release
The phase includes an agreed evaluation set and acceptance threshold. We do not substitute a provider benchmark for performance on your workflow.
Fixed-price phase
Once the workflow, controls, evaluation, and handover are agreed, the phase price is locked in writing.
Useful next steps
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Read moreLLM integration connects a language model to an application's data, interfaces, or business workflow. Production work includes prompt and context design, structured output validation, tool permissions, rate limits, fallbacks, evaluation, cost tracking, monitoring, and user states for uncertain or failed output.
Use retrieval-augmented generation when answers must draw from private or changing knowledge and cite the supporting source. A plain prompt may be enough when the task uses information already supplied in the request. RAG does not replace output evaluation or permission controls.
We define an output schema and business rules, test them against representative inputs, reject or retry invalid responses where safe, and route uncertain cases to a person. The release threshold is measured on the buyer's evaluation set rather than inferred from a model benchmark.
We can isolate provider-specific calls behind a shared application boundary and keep prompts, schemas, and evaluations versioned. Switching still requires testing because models differ in behaviour, tool formats, latency, safety controls, and price. Provider abstraction reduces migration work but does not make providers interchangeable.
A focused first feature starts around $15,000 and usually takes 6 to 10 weeks. Retrieval, several tools, sensitive data, high traffic, custom interfaces, and stricter evaluation or approval requirements increase the scope. Ongoing model and hosting charges remain separate and are estimated from expected usage.
Work with us
Bring the prototype, representative inputs, and the failure that matters most. We will scope one measurable production release.