Operations platform with AI

A CRM brings together information that a team needs to turn into next actions: unattended opportunities, stalled pipeline stages, proposals requiring attention and the actual meaning of business indicators.

• I led architecture, implementation and validation to connect the interface, API and business rules.
• My goal was a coherent working experience in which leads, tasks and proposals behave as parts of one product rather than unrelated screens.

AI assistance belongs inside that workflow, interpreting questions and using permitted information.

That requires more than sending a prompt: context, tools, authorization, persistence and the behavior of failing dependencies must all be designed.

The public demonstration includes demo access, a dashboard, leads, pipeline, tasks, proposals, automations, campaigns, analytics and an assistance area.

• Start with the dashboard, open the lead list and inspect how the interface presents stages, owners and next actions.
• Then explore settings and different views of the data.
• This path reveals product organization, information hierarchy and the connections between modules.

All records are synthetic, changes are temporary and external services are simulated.

• The environment does not query a real business or prove the execution of a live LLM provider.
• It demonstrates the product experience; this briefing separately explains implemented architecture and the controls validated in component tests.

The React interface communicates with a Fastify API through TypeScript contracts.

• In the implemented architecture, PostgreSQL provides persistence and OpenRouter connects the application to language models.
• A question enters through an authenticated route, the server composes the available context and the model may request semantic tools.
• Tool responses return to an assistance loop with defined execution limits.

I separated presentation, interpretation and data access.

• An interface is not a security boundary, and a prompt instruction cannot replace authorization.
• The API derives access scope from the authenticated user and keeps operations within system rules regardless of what the model is asked to do.

Tool calling exposes bounded operations instead of allowing a model to invent SQL queries.

• A tool represents a known intention, such as retrieving authorized CRM information; the backend performs parameterized queries and enforces permissions.
• This reduces the distance between a natural-language request and an auditable operation.
• It also allows tool behavior to be tested independently of variability in generated responses.

Persistent memory and lexical SQL retrieval are implemented in source.

• Database execution was not verified in this audit: queries and mapping were tested with substitutes rather than a real PostgreSQL instance.
• This is not embedding-based or vector retrieval.

Sensitive fields are sanitized before becoming model context.

• Access scope comes from the server rather than an identifier selected by a visitor or a suggestion generated by AI.
• Controls also constrain what a tool can return.
• Authentication, authorization, controlled queries and information minimization participate in the same path as complementary safeguards.

Public examples and imagery are independent of clients.

• A synthetic account enables exploration without credentials from a real operation.
• I do not publish private logs, customer data, internal identifiers, infrastructure topology or extracts that could identify an organization.
• Confidentiality applies to downloadable assets and browser bundles as well as visible copy.

LLM integrations must account for unavailable services, unexpected responses and invalid credentials.

• The implemented handling distinguishes recoverable failures from errors that should not be retried.
• The tool loop has a limit so a conversation cannot become unbounded execution.
• Tokens, context and cost also have explicit state, including the situation where there is insufficient information to report cost.

Missing measurement is not presented as a measured zero.

• This decision improves operational interpretation and prevents the interface from implying precision that the system does not have.
• Classifying failures and limiting calls makes integration easier to reason about, but does not guarantee that a language model will never hallucinate.

I preferred semantic tools and backend access rules over unrestricted SQL.

• This provides control, testability and a smaller operational surface, while requiring an explicit contract for each additional capability.
• I preferred text retrieval for the available scope over claiming an unimplemented vector architecture.
• That keeps the solution simple and aligned with the problem while leaving a clear reason to consider embeddings later.

I also separated the public demonstration from the real architecture.

• A repeatable demo with synthetic data can be explored without changing a business operation.
• The briefing provides engineering depth, while the public environment makes the product experience safely accessible.

The assistant slice passed 23 local tests in the October 4, 2026 audit.

• Scenarios cover permissions, read tools, model configuration, retry, rejection of arbitrary SQL and sanitization.
• This supports the behavior of components exercised with substituted dependencies.
• The count describes that specific audit slice rather than certification of the complete application.

The execution did not certify a real PostgreSQL instance, external provider, load behavior or production AI operation.

• I do not infer those conditions from the existence of code.
• The demonstration provides a separate kind of evidence: public access and functional navigation with synthetic records.

To extend assistance, I would first define reference questions, response criteria and cases where the model must acknowledge missing information.

• That set would make it possible to compare changes in context, tools and models.
• Embeddings, hybrid retrieval or reranking would be introduced only with a testable improvement hypothesis.
• These are potential next steps, not capabilities claimed for this delivery.

A hiring conversation can start from a demo screen and continue through the API contract, authorization rules and failure handling.

That continuity between product and engineering is what I want to make assessable: concrete decisions, explicit boundaries and an experience you can actually explore before discussing a project.

Completed within the audited slice: 23 local backend tests, with substituted database and model transport.

• Partial evidence: SQL migrations and persistence implementation.
• End-to-end provider and database operation is not certified here.

The public demonstration uses synthetic records and simulated integrations.

• A sanitized evidence report includes scope and digests; private source and original logs are not published.
• No measured commercial impact is claimed.