LLM Integration Services
LLM integration for features that must survive real users.
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.
Trusted by
The brief
Start with what is not working.
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 provider evaluation, structured outputs, tool permissions, fallbacks, and monitoring for OpenAI, Anthropic, Google, and multi-model architectures.
What to remember
- An API call proves the model responds; it does not prove the feature returns valid output, stays within budget, or handles failures.
- Structured output with validation is the first requirement; the apostrophe that breaks the JSON is the test case.
- Tool use runs inside permission boundaries, with evaluation and version control on prompts and models.
- Fallbacks, cost, and monitoring are part of the integration, not an afterthought.
The model returned valid JSON until a customer used an apostrophe.
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.
What the control layer must prove
- output the product can validate
- Schema
- Syntax and business rules before downstream use
- actions limited by application permissions
- Tools
- The model proposes; deterministic code authorizes
- provider and prompt changes compared before release
- Eval
- Representative inputs, failures, latency, and cost
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Choose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
- 02
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
- 03
Build
Own the system when the workflow shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
- 04
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Custom LLM integration pays off when one model response becomes part of a real workflow.
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
What turns a model call into a product feature
Structured output and validation
Schemas constrain the response shape. Application rules then check required values, ranges, references, and permitted operations before output reaches the database or another system. Failed validation follows an explicit retry or review path.
Tool use with permission boundaries
The model may propose a lookup, draft, update, or external request through a narrow tool contract. The application checks identity, permissions, arguments, and approval rules before executing consequential work.
Evaluation and version control
Representative inputs, expected properties, and known failure cases become an evaluation set. Prompts, models, and schemas are versioned so a provider or prompt change can be compared before release.
Fallbacks, cost, and monitoring
Timeouts, rate limits, invalid output, and provider incidents receive visible product states. Logs protect sensitive fields while tracking latency, errors, token use, and cost by feature or tenant.
OpenAI, Claude, Gemini, or a multi-model boundary?
Provider approval can narrow the implementation, but it does not remove the need to evaluate the workflow. We test candidate models on the same representative cases and record output quality, latency, refusal behaviour, structured-output reliability, tool behaviour, regional availability, data controls, and unit economics. The application keeps its schemas, evaluation set, policy, and business rules outside the provider call so a later model change is a measured release rather than a rewrite.
Provider-specific implementation choices
| Use it when | What still needs engineering | |
|---|---|---|
| OpenAI or Azure OpenAI | The approved stack or task evaluation points to OpenAI models and tool support | Structured outputs, tool authorization, context boundaries, version pinning, rate limits, and fallback behaviour |
| Anthropic Claude or AWS Bedrock | Claude performs best on the task, or Bedrock fits an existing AWS procurement and access model | Direct Anthropic versus Bedrock controls, retention and access review, tool permissions, prompt-injection tests, and provider concentration risk |
| Google AI or Vertex AI | Gemini wins the task evaluation, multimodal or long-context work matters, or the product already runs on Google Cloud | Google AI versus Vertex deployment, IAM and regional controls, context-cost tests, multimodal validation, quotas, and fallback design |
| Multi-model routing | Different tasks have materially different quality, latency, availability, or cost requirements | A shared request boundary, provider adapters, routing evidence, comparable evaluations, observability, and an explicit degraded mode |
If OpenAI is already selected
We integrate the approved model without coupling the rest of the product to a provider-shaped response. Tool calls pass through typed application contracts, structured output is validated before use, and prompt and model versions are evaluated together. The release plan also covers rate limits, provider incidents, context leakage, and the evidence required before a future model version replaces the current one.
If Claude or AWS Bedrock is already selected
Direct Anthropic access and Claude through Bedrock can differ in procurement, regional availability, identity, logging, and operational controls. We make that deployment choice explicit, then test long-context behaviour, tool use, structured output, and prompt injection on the real workflow. A provider-specific implementation can still preserve a narrow adapter, versioned evaluations, and a fallback path so provider concentration remains an accepted business decision rather than a hidden dependency.
If Gemini or Vertex AI is already selected
Google AI can suit a direct application integration; Vertex AI can fit teams already operating with Google Cloud IAM, regions, audit controls, and data services. We validate the exact deployment rather than relying on a model-family claim. Long context and multimodal inputs are tested against representative documents, images, audio, or video, including latency, context cost, output grounding, quotas, and failure states.
Does the feature need prompting, RAG, or fine-tuning?
Choose the lightest adaptation that clears the test
| 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
From working prompt to measured product feature
The release threshold is agreed before production code expands.
- Phase 101
Define the decision and test set
Choose one workflow, collect representative and adversarial inputs, and agree quality, latency, cost, permission, and escalation thresholds.
- Phase 202
Prove the integration pattern
Test prompting, retrieval or tools, structured output, and provider behaviour against the evaluation set. Record the baseline rather than judging a few good responses.
- Phase 303
Develop the production controls
Add authentication, permissions, validation, retries, fallbacks, tracing, cost limits, and usable states for slow, uncertain, or failed output.
- Phase 404
Release and monitor
Roll out to a bounded user group, inspect failures, compare prompt and model versions, and expand only after the feature clears its threshold.
Proof from a model inside a real product
RaftLabs developed Perceptional, a conversational AI interview platform, using Anthropic Claude through AWS Bedrock. The product turned an interview conversation into a structured summary and required the same provider, context, output, and workflow controls described on this page.
That is evidence from one product, not a promise of the same output quality for a different model, workflow, or data set.
- The demo becomes the test plan
- A handful of curated prompts says little about production behaviour. Use representative inputs, failure cases, and measurable acceptance rules.
- Model output is trusted after JSON parsing
- Valid syntax can still violate business rules. Validate meaning, permissions, references, and ranges before accepting a response.
- The model controls consequential work
- Tool access needs narrow permissions, argument checks, audit records, and human approval where a mistake can charge, publish, delete, or disclose.
- Provider abstraction is called portability
- A shared interface helps, but another model changes behaviour. Every switch requires the same evaluation and release discipline as a code change.
Start with one workflow and one evaluation set
Bring the working prompt or prototype, representative inputs, provider constraints, and the failure that matters most. We will define the smallest production release, the controls around it, and the evidence it must clear before more tools, providers, or user groups are added.
What it costs
Every project starts at $10000.
The first phase covers one model-backed feature, with structured output validation, permission boundaries, and evaluation wired in.
Starting investment
Starts at $10000
Start with the feature where an API call alone does not prove the output is valid. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Compare the implementation choices
Work with us
Show us where the model feature stops behaving.
Bring the prototype, representative inputs, and the failure that matters most. We will scope one measurable production release.
- The workflow, representative inputs, and output the product must accept
- Approved providers, deployment constraints, data boundaries, and tool permissions
- Quality, latency, cost, fallback, and human-review thresholds
Common questions
LLM 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.
Put business logic behind an internal interface, not a vendor SDK. Keep prompts and routing rules in config, pin explicit model versions, and hold a regression set of real inputs. Swapping providers then becomes a config change, but it still requires the same testing because models differ in behavior, latency, and cost.
Confirm training opt-out in writing, set an explicit retention window, and mask sensitive fields before they leave internal systems. A secure environment such as Azure OpenAI Service or a private VPC, plus data residency controls, keeps your data from being used to train public models.
The scope depends on the workflow, context and retrieval needs, tool permissions, provider requirements, evaluation set, traffic, sensitive data, reliability targets, and changes required in the host product. We define one measurable feature and its operating controls before expanding the integration.