AI ENGINEERING · APIS · BUSINESS SYSTEMS
Build AI agents, knowledge assistants, and connected applications around the way your team works. OdooMatrix helps businesses worldwide move from a defined problem to an evaluated, integrated feature.
DESIGNED AROUND YOUR STACK
Source permissions. Clear tool access. Review when it matters.
WHAT WE BUILD
Choose a focused feature or bring several capabilities into one product. Each starts with a defined outcome and the data needed to deliver it.
Build assistants that use selected tools to look up information, prepare a response, or carry out a defined task. A sales assistant could retrieve an order and draft a follow-up; a person can approve the action before it updates a business system.
We define the available tools, validate their inputs, limit permissions, and set stopping conditions. Repeated requests, failed API calls, and a handoff to your team are part of the design.
Connect a chatbot or internal search experience to approved policies, product information, manuals, or support content. Retrieval-augmented generation (RAG) finds relevant material and supplies it to the model so an answer can include references to its sources.
We plan document ingestion, updates, retrieval quality, and access checks. The assistant should say when the available material does not support an answer. RAG can improve grounding; it does not eliminate incorrect answers.
Compare RAG, fine-tuning, and toolsTurn invoices, purchase orders, forms, and incoming emails into structured information for your existing process. Optical character recognition (OCR) can read scanned text; extraction identifies fields, and validation checks them against your business rules or existing records.
Low-quality scans, missing fields, duplicates, and mismatched totals need a review route. We assess representative documents and preserve a link to the original so your team can check an extracted value.
Add a support chatbot, an internal assistant, or a voice interface to a website or application. Connect the conversation to relevant knowledge and permitted actions, such as checking an order status or preparing a service request.
Agree on supported languages, response time, conversation history, and escalation to a person. For voice projects, review transcription quality, consent and recording requirements, and how interruptions or misunderstood speech are handled.
Build AI features into a new product or extend an existing application: summarize long records, draft content for review, classify incoming messages, or help users find information. We connect the model to the application interface, backend, and data it actually needs.
Model selection considers output quality, speed, usage cost, and deployment requirements. OpenAI, Anthropic, or an appropriate open model can be evaluated against the task. Custom model training is a separate decision, based on the available data.
Bring document extraction, knowledge assistance, or draft preparation into an Odoo, ERP, or CRM process. A reviewed result can feed an approved record update through an integration, while the business system keeps ownership of its transaction data.
We check native features first, then assess custom modules or API integration for your version, edition, hosting, and subscription. Odoo, SAP, Dynamics 365, NetSuite, and other platforms have different access requirements.
Explore Odoo integration planningCUSTOM AI PRODUCTS
Product options we can build around your users, data, and applications. The scope, integrations, evaluation, and deployment are agreed for your project.
Create a support workspace or website assistant that uses approved help content and relevant customer context. Let your team review suggested replies and take over conversations when needed.
Give employees one place to ask questions about approved company information. Connect selected document sources and preserve the permissions that determine what each person can see.
Build a workspace for invoice intake, extraction, validation, and review. Match the agreed fields against business records, then prepare approved data for an available ERP or accounting integration.
Bring relevant lead, opportunity, and conversation context into a sales workspace. Help the salesperson prepare a follow-up and review the next action before a message is sent or a record is updated.
We assess model quality, supported features, usage costs, and the access plan for the selected provider.
A USEFUL STARTING POINT
These approaches can work together. Start with the smallest useful capability, then evaluate whether more complexity improves the result.
Use conditions, calculations, scheduled jobs, or API calls when the same input should produce the same result. Define missing-field handling and retries. A model is useful only if part of the task requires interpreting variable information.
Retrieve relevant information from approved sources and provide it to a model. Plan source updates, access filtering, and answer references. Test both answerable questions and questions the content cannot support.
Expose selected application functions to an assistant: retrieve a record, prepare a draft, or request an approved update. Validate arguments and authorization in the application. Define run limits, failure handling, and any approval steps.
Review the AI features in your installed ERP, CRM, or support tools. Configuration may address the requirement. Custom development can then focus on missing connections, business rules, or application behavior.
Fine-tuning is another option for a defined output behavior when suitable training and evaluation data exist. It changes model behavior; current company information still needs an appropriate retrieval or integration strategy.
AUTOMATION & INTEGRATION
AI is one part of the application. Reliable connections and clear business rules make the result useful.
Read the API synchronization guideConnect ERP, CRM, eCommerce, payment, and custom systems through available interfaces. Define which system owns each field, how records are matched, and how updates recover when a service is unavailable. Retries and duplicate prevention need to work together.
A file conversion, an approval route, or a scheduled report may need a script rather than a model. Combine conventional automation with AI only where the input calls for it. Keep a visible failure queue and an agreed way to review exceptions.
Apply the user’s permissions when retrieving information or calling a tool. Agree on what is logged and retained, isolate secrets from prompts, and test unexpected or misleading instructions in retrieved content. These checks belong in the application design.
FROM FIRST BUILD TO EVERYDAY USE
Agree on what success means before scaling a feature. Quality, response time, operating cost, and recovery all matter.
Map the current task, its users, and the data involved. Review sample inputs, API access, deployment constraints, and which actions need approval. Agree on a first milestone and acceptance checks.
Build a focused prototype and compare it with the current process. Evaluate normal, incomplete, and difficult inputs. Check answer quality, tool behavior, response time, and model costs before extending the scope.
Integrate authentication, logging, monitoring, retries, and a fallback route. Document the setup and review process. Plan regression checks when prompts, models, source documents, or connected applications change.
AI PROJECT QUESTIONS
Practical answers about models, data, integrations, and scope.
Use representative documents and questions, with expected answers or source references reviewed by someone who knows the subject. Include incomplete inputs, questions the content cannot answer, and users with different access. Compare retrieval quality, answer usefulness, response time, and operating cost.
We help build AI agents, RAG knowledge assistants, document processing, chat and voice interfaces, and generative AI features, with API connections to business systems. A project can cover a focused feature or the application, evaluation, deployment, and ongoing support around it.
Yes, where the required modules or APIs are available. We check your version, edition, hosting, permissions, and subscription before proposing a connection. Native features may meet part of the need; custom work should address a clear gap.
Often a project can start with an existing model, clear instructions, retrieval from approved information, or tool calling. Fine-tuning may help a defined behavior when there is suitable training data. We compare approaches against representative examples before recommending it.
We agree which information the feature needs, who may access it, and which model or hosting options are acceptable. The design considers source permissions, data retention, logging, and provider terms. A private deployment is assessed against your infrastructure and operating requirements.
Cost and delivery time depend on the feature, integrations, data readiness, review process, and deployment requirements. We scope a first milestone with acceptance checks, then estimate development and ongoing model, hosting, and maintenance costs. There is no universal price or timeline.
Yes. Rules, scripts, scheduled jobs, and APIs can be a better fit for predictable tasks. We can automate one step and introduce AI only where variable language, documents, or another defined need justifies it.
The listed AI products are custom development offerings. We define the users, features, data sources, integrations, evaluation, and deployment with you.
LET’S BUILD SOMETHING USEFUL
Tell us who uses the process, which systems are involved, and what a good result looks like. A few sample inputs with sensitive details removed are a useful starting point.
