AI integration services for the software you already run
We add large language model capabilities to existing applications and data flows through a separate AI service that respects your users' permissions, logs every call, and can be switched off with a flag - without rebuilding the system around it.
- access
- User's own permissions
- calls
- Every one logged
- rollout
- Behind a switch
- Application: Your appunchanged
- Orchestrator: AI serviceprompts
- Guardrail: Permissionsuser's access
- External service: Modelhosted / private
01 / What AI integration means
AI inside your software, not another tool beside it
Definition · AI integration
Connecting AI models - usually large language models - to software and data you already operate, so that features such as search, summaries, drafting or classification work inside existing screens and processes, under the same access rules.
- S1
A product you sell
AI features for every customer of your SaaS or platform, isolated per customer and priced into your plans.
- S2
An internal system
A CRM, ERP, case management or document system where staff would save time with summaries, search or drafts. For the CRM case, see CRM integration services.
- S3
A data workflow
Pipelines that move records between systems and could classify, enrich or extract along the way.
- S4
An existing chatbot
A scripted bot that needs a language model and your knowledge sources behind it. See AI chatbot development.
02 / Integration patterns
Four ways LLM integration services fit into existing software
Most projects use one or two of these. The choice depends on where users work and where the data lives.
- P1
AI features in your interface
Buttons and panels inside existing screens - summarise this case, draft this reply, explain this report - calling the AI service behind the scenes.
- P2
An AI service behind your API
Classification, extraction or generation exposed as internal endpoints that any of your applications can call, with one place for prompts, models and limits.
- P3
Search and Q&A over company data
Retrieval-augmented generation across documents and records, filtered by each user's permissions, with sources shown in the answer.
- P4
LLM steps inside data pipelines
Records classified, normalised or enriched as they move between systems - with validation and a review queue for uncertain results.
03 / Reference architecture
How a request travels through an AI integration
The key design decision: permissions are applied before anything reaches the model, so the AI can never reveal what the user could not open themselves.
- Application: Your applicationusers, screens, data
- Guardrail: Feature flagon per team or customer
- Database: Company dataCRM · ERP · files · wiki
- Orchestrator: AI serviceprompts · routing · caching · validation- request
- Search index: Search indexdocuments, records, embeddings
- Guardrail: Permission filteronly what this user may see
- Log: Audit log & evaluationevery request · tests on every change
- AI model: Language modelprovider API, your cloud or your servers
- External service: Provider termstraining excluded · retention set
04 / Deployment options
Where the model runs, and what private AI for business really involves
Each option is a trade-off between capability, cost, effort and data control. As an AI integration company we are not tied to a provider: we test the candidates on your tasks before recommending one.
| Option | Where data goes | Typical strengths | Choose when |
|---|---|---|---|
| Hosted model API | To the model provider, under business terms that can exclude training and limit retention | Most capable models, no infrastructure, fast to start | Provider terms satisfy your data rules |
| Model in your cloud account | Stays within your cloud tenancy and region | Strong models with your cloud's security controls and contracts | Data must stay in your cloud or region |
| Self-hosted open-weight model | Stays on infrastructure you control | Maximum control; predictable cost at high volume | Data cannot leave your environment at all |
Private AI
Private AI is a feasibility question, not a product
Self-hosting needs suitable hardware, people to operate it, and usually accepts a less capable model. We assess it with a small benchmark on your tasks and a cost comparison before anyone buys servers.
05 / LLM development
What LLM development services include in practice
For business applications, LLM development is engineering around existing models - not training a new one from scratch, which is rarely worth the cost.
- Prompt and tool design, versioned and tested like code.
- Retrieval - chunking, indexing and ranking your content so the right passages are found.
- Evaluation - test sets and automated scoring for every change.
- Fine-tuning - only when measurements show it beats retrieval and prompting for your task.
- Prompt v14 · shorter summariesevaluation: 46 / 48 casesreleased
- Model upgrade candidateevaluation: 41 / 48 · slowerheld
- Index: contracts addedpermission tests passedreleased
06 / Security and access
Designed for your security review
Documented in a short architecture and data-flow note that your security team can review before launch.
User-level permissions
- Retrieval filtered per user
- Tested with permission cases
Least-privilege access
- Narrow service credentials
- Secrets in your vault
Personal data
- Redaction where not needed
- Fields excluded from indexing
Logging
- Requests, sources, outputs
- Retention per your policy
Input safety
- Untrusted content separated from instructions
- Output checks before display
Vendor terms
- Training and retention reviewed
- Region and processor details
07 / How an integration runs
Added step by step, behind a switch
Our AI integration services treat AI integration into existing software as lower-risk when the new service is separate, observable and reversible - so the process is built around those three properties.
- stage
System & data review
Your architecture, data sources, access model and the features users would value most.
out →Integration options
- stage
Architecture & feasibility
Deployment option, data flow and permissions designed; candidate models tested on your tasks.
out →Architecture note, benchmark
- stage
Prototype behind a flag
The AI service integrated into a test environment and shown to a small group.
out →Working feature, evaluation
- stage
Staged rollout
Enabled per team or customer, with monitoring on quality, latency and cost.
out →Production feature
- stage
Monitoring & handover
Dashboards, runbook and training for your engineers to own it.
out →Documentation, handover
08 / Budget
What drives the cost of AI integration
The state of your existing system matters as much as the AI. A system review comes first, so the estimate reflects what is actually there.
Existing architecture
Clean APIs make integration simple; tightly coupled or undocumented systems take longer and may need API integration work first.
Access model
Fine-grained permissions must be mirrored in retrieval and tested.
Deployment option
A hosted API is quickest; self-hosting adds infrastructure and operations.
Usage volume
Requests per day and text per request set the running cost.
09 / Questions
Frequently asked questions
How do you integrate AI into existing software?
Usually through a separate AI service that your application calls like any other internal API. The service holds prompts, model access, retrieval and logging; your application keeps its own logic, data and user interface, with new AI features added where users need them. This avoids rewriting the existing system and lets the AI part be tested, monitored and switched off independently.
What is LLM integration?
LLM integration means connecting a large language model to your software and data so it can perform tasks inside them: summarising records, answering questions from your documents, classifying incoming items, drafting text. The integration work is mostly around the model - retrieving the right data, enforcing who may see what, validating output, and controlling cost.
Can we have a private AI that does not send data outside our company?
Often, yes, but it is a trade-off to assess rather than a default. Options range from hosted models with contractual data protection, to models deployed in your own cloud account, to open-weight models running on your infrastructure. Self-hosting gives the most control but requires capable hardware and usually means a less capable model. We test the options on your tasks before recommending one.
Will our data be used to train the model?
It should not be. We use deployments whose terms exclude training on your requests, or keep inference inside your environment. Retrieval-based designs read your data at request time and do not need to put it into the model at all.
Do you offer LLM development, such as building our own model?
We develop applications on top of existing large language models: prompt and tool design, retrieval, evaluation, and fine-tuning where measurements justify it. We do not pretrain foundation models from scratch, which is rarely the right investment for a business application.
We already have a chatbot. Can you integrate AI into it?
Yes. We can connect an existing chatbot to a language model and your knowledge sources, or replace its scripted flows step by step. If the assistant itself needs redesigning, see AI chatbot development.
Can AI be added to legacy systems without an API?
In most cases. Options include reading from a database replica or exports, adding a small API layer in front of the legacy system, or processing documents and emails the system already produces. We assess what is safe and maintainable during the system review.
Start node · your brief
Want AI inside the software you already run?
Tell us what the system does, who uses it, where the data lives and which feature you have in mind. We'll outline an integration approach and the questions to settle first.