Choosing a model
Hosted and open-weight models differ in quality, context length, cost, speed and data terms, and the best choice varies by task.
AI & Automation · LLM Integration
LLM integration services for software you already run
We add large language model capability to your existing product, ERP or internal tools in a way your engineers can test, monitor and maintain, with clear limits on what the model is allowed to do.
What is LLM Integration?
LLM integration services embed a large language model into an existing application or business system so it can summarize, classify, extract, draft or answer questions as part of normal use. The work includes choosing between hosted and self-hosted models, designing prompts and structured outputs, grounding the model in your data through retrieval, adding guardrails and building an evaluation suite. It treats the model as a component with known failure modes, not as a source of truth.
Language models are probabilistic: the same input can produce different outputs, and fluent output can still be wrong. Integrations that pass free text from the model straight into a database, a customer email or a business decision inherit that unpredictability. Reliable integration constrains what goes into the model, validates what comes out and measures behavior on a fixed test set whenever the prompt, data or model changes.
What makes LLM integration harder than an API call
Unstructured output
Downstream code needs predictable fields, but models return text that may not match the expected format.
Hallucination and stale knowledge
Without your data in context, a model answers from general training, which may be incorrect or out of date for your business.
Silent regressions
A small prompt edit or provider model update can change behavior in ways nobody notices without regression tests.
Our LLM Integration service
Model selection and benchmarking
Testing hosted models from providers such as OpenAI, Anthropic or Google and open-weight models against your tasks, comparing quality, latency, cost and data handling.
Prompt and output design
Versioned prompt templates, examples and structured output schemas, with validation and retry when output doesn't conform.
Retrieval-augmented generation
Grounding the model in your documents or database records at request time, so answers reflect your current data.
Guardrails
Input checks, topic limits, output validation, refusal behavior and restricted tool access appropriate to each feature.
Evaluation suite
A test set of real inputs with expected outputs or scoring criteria, run automatically before prompt, model or data changes go live.
Hosting and operations
Provider API integration or self-hosted deployment on your infrastructure, with logging, cost tracking and monitoring.
Included capabilities
- Hosted or self-hosted model options
- Model comparison on your own tasks
- Versioned prompt templates
- JSON and schema-validated outputs
- Function and tool calling with validated arguments
- Retrieval over documents and records
- Input and output guardrails
- Automated regression evaluation
- Streaming responses in the interface
- Token usage and latency monitoring
What LLM Integration changes for your business
Behavior you can measure
An evaluation suite shows how the integration performs and catches regressions before users do.
Predictable outputs for your code
Schema validation means downstream systems receive data in the format they expect.
Model independence
A service layer lets you swap or mix models as quality and pricing change.
Fits your existing architecture
The model becomes a component of your system rather than a separate tool.
Where it makes the biggest difference
Summaries inside a product
Summarizing long records, threads or reports where users already work, with links back to the source material.
Structured extraction for back-office systems
Turning free-text notes, emails or descriptions into validated fields that existing systems can store and process.
Question answering over your data
Letting users ask questions about records or documents in an existing portal, grounded through retrieval.
Drafting assistance in ERP and CRM
Drafting follow-up emails, quotation notes or product descriptions from record data for staff to edit and send.
Seven steps from task definition to maintained feature
The test set comes first, because it is the only fair way to choose a model and know when a change has made things worse.
Identify the task
Define the exact function the model performs, its inputs, outputs and what happens with wrong output.
Build a test set
Collect representative real inputs and agree what a correct or acceptable output looks like.
Benchmark models
Compare candidate models and prompting approaches on the test set, including cost and latency.
Integrate
Implement the model behind a service in your architecture, with retrieval, schemas and guardrails.
Evaluate and harden
Run the full suite, test adversarial and edge-case inputs, and tighten validation.
Release behind a flag
Enable the feature for a subset of users and monitor quality, cost and feedback.
Maintain
Re-run evaluations when models or data change, and review logged failures regularly.
What it connects to
LLM Integration
- Hosted LLM provider APIs
- Open-weight models on your servers or cloud
- PostgreSQL with pgvector
- Odoo and ERP data via API
- Existing web and mobile back ends
- Document stores and file systems
- Observability and logging tools
Built with
Hosted or self-hosted models
| Criterion | Hosted model API | Self-hosted open-weight model |
|---|---|---|
| Setup effort | Low | Higher; needs GPU infrastructure and operations |
| Data location | Sent to provider under its terms | Stays in your infrastructure |
| Cost pattern | Pay per token | Fixed infrastructure cost |
| Model capability | Access to providers' largest models | Depends on the model size you can run |
| Best fit | Most business features | Strict data residency or high, steady volume |
Security, quality and scale
Hosted vs self-hosted choice
Sensitive workloads can run on open-weight models inside your infrastructure, so prompts and data stay there.
Context minimization
Only the records and fields needed for a request are included in the prompt.
Prompt injection controls
Retrieved and user-supplied text is separated from instructions, and model tool access is restricted.
Logging with redaction
Requests and responses are logged for debugging and audit, with personal data redacted where required.
What working with us looks like
Evaluation-driven
We build the test set before choosing the model, so decisions are based on your tasks rather than general benchmarks.
Model-agnostic
We work with hosted and open-weight models and design integrations that aren't locked to one provider.
Systems integration background
We integrate with Odoo, custom back ends and third-party systems, so the model connects to real data.
Clear about limits
We document which tasks the model handles reliably and which need human review.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
LLM Integration: questions we hear most
Which LLM should we use?
The right model depends on the task, so we benchmark several on your own test set before choosing. Options include hosted models from providers such as OpenAI, Anthropic or Google and open-weight models you host yourself, compared on quality, latency, cost and data handling.
How do you reduce hallucinations?
We ground the model in your data through retrieval, restrict it to well-defined tasks, validate outputs against schemas and business rules, and instruct it to say when it doesn't know. These measures reduce errors but don't eliminate them, so high-impact outputs should be reviewed by a person.
Can we keep our data off third-party servers?
Yes, by self-hosting an open-weight model on your own servers or private cloud. This keeps data in your infrastructure but requires suitable hardware and operational effort, and the most capable hosted models may perform better on some tasks.
Should we fine-tune a model?
Usually not as a first step. Good prompts, examples and retrieval solve most business tasks, and fine-tuning is considered when evaluation shows a consistent gap in format or style that those methods can't close.
What drives the cost of LLM integration?
Development cost depends on the number of features, the integration work with your systems and the evaluation effort. Running cost depends on request volume, prompt and output length, and model choice, or on infrastructure if self-hosted.
How is LLM integration different from building a generative AI application?
LLM integration adds language-model capability to software you already have. Generative AI application development builds a new product whose main purpose is generating content, including its interface, billing and user workflows.
Next step
Bring one feature and twenty real examples
Tell us where you want a language model in your product and share sample inputs. We'll benchmark approaches and tell you what's reliable enough to ship.