AI systems, agents, and automation for workflows that already matter
Infitics builds LLM integrations, RAG systems, copilots, chatbots, AI agents, MCP integrations, and workflow automation—with evaluation, permissions, monitoring, fallbacks, and human approval designed in from the start.
The right AI feature earns its place
A convincing demo is not the business case. We first determine whether AI is the right tool, what better looks like, and what must never be left to probability.
A defined job
Start with a recurring user or operational problem where language, ambiguity, or unstructured information creates real friction.
- Clear user and workflow owner
- Known inputs and desired output
- Current cost, delay, or failure mode
A measurable baseline
Define acceptable quality before selecting a provider or designing prompts, then test against examples that represent the real work.
- Representative evaluation set
- Accuracy and usefulness criteria
- Latency and cost boundaries
A safe boundary
Separate suggestions from actions. Deterministic rules, permissions, and human review stay in control where consequences are material.
- Scoped access to data and tools
- Approval for consequential actions
- Clear fallback and refusal behavior
An operating owner
AI behavior changes with data, models, prompts, and usage. A production feature needs ownership after the first release.
- Quality and incident review
- Cost and usage monitoring
- Feedback and improvement loop
LLM integration, RAG, copilots, and AI agents—chosen for the workflow
We choose the simplest architecture that can meet the quality, security, and operating requirements of the workflow.
Understand and assist
RAG and grounded knowledge
Find relevant internal knowledge, preserve source references, and make uncertainty visible instead of treating fluent output as fact.
Document workflows
Classify, extract, compare, summarize, or draft from unstructured material with validation around important fields.
Product copilots and chatbots
Add context-aware assistance inside existing software so users remain in control, inside the workflow they know, and able to verify or escalate an answer.
Decide and act carefully
Structured decisions
Constrain outputs to known formats, validate them in software, and route uncertain cases to a person or deterministic rule.
AI agents with controlled tool use
Let the system look up or change information only through narrow, authenticated tools with explicit permissions and audit history.
Workflow automation with explicit state
Coordinate bounded steps with checkpoints, retries, state, and approval rather than relying on one opaque autonomous loop.
From a useful prototype to a controlled production system
The model is one dependency. The surrounding product and engineering controls determine whether the feature can be trusted in real use.
Evaluation
Automated and human review against real examples, including edge cases and adversarial inputs.
- Task-specific quality criteria
- Regression tests for prompts and models
- Release gates based on evidence
Security and permissions
Least-privilege access, separation between users and accounts, and protection against unsafe instructions or data exposure.
- Authentication and authorization
- Data handling boundaries
- Tool allowlists and audit events
Reliability and cost
Timeouts, retries, caching, provider abstraction, fallbacks, and budgets shaped around the value of each request.
- Failure and degradation paths
- Latency and token budgets
- Provider and model routing where useful
Observability and feedback
Trace what happened, understand why users override it, and improve based on production evidence.
- Quality, usage, cost, and error signals
- Privacy-aware traces and review
- User feedback tied to evaluation
MCP integrations: where a shared protocol helps—and where it does not
Model Context Protocol can standardize how AI clients discover tools and resources. It does not replace application security, workflow design, or sound judgment about what the model may do.
Use a shared protocol when it creates leverage
A protocol layer can help when several AI clients need the same approved tools or resources, or when provider portability is an important requirement.
- Reusable, well-defined tool contracts
- Central permission and audit boundaries
- Consistent access to approved resources
Use a direct integration when it is clearer
For one narrow product workflow, an authenticated application service may be simpler to understand, test, operate, and secure.
- Fewer moving parts
- Explicit application ownership
- No protocol introduced without a need
A path from opportunity to operated capability
Each stage produces evidence for the next decision. The goal is not to force every idea into production.
Frame the job and the risk
Map the workflow, users, data, consequences, existing baseline, and the boundary between probabilistic and deterministic behavior.
- Opportunity and feasibility assessment
- Data and security constraints
- Success and stop criteria
Build the evaluation before the feature
Create a representative test set and compare the simplest viable approaches against quality, latency, and cost requirements.
- Baseline and evaluation harness
- Architecture experiment
- Go, reshape, or stop decision
Integrate with controls
Place the capability inside the real product, with permissions, structured outputs, fallbacks, auditability, and human review.
- Product and systems integration
- Security and failure handling
- Staged release to real users
Observe and improve
Review production behavior, feed corrections into evaluation, manage cost, and adapt when the data or underlying models change.
- Quality and incident review
- Cost and performance optimization
- Ongoing product iteration
AI engineering questions
The responsible answer is often conditional. These are the conditions we clarify before implementation.
What do LLM integration services include?
They can include workflow discovery, model and architecture evaluation, RAG, structured extraction, copilots, chatbots, agents, MCP or direct tool integration, product implementation, permissions, evaluation, observability, fallback behavior, and operational handoff. The exact scope should follow the workflow and its risk.
Is an AI agent the same as workflow automation?
No. An agent uses a model to choose among permitted steps or tools. Many workflows are safer and easier to operate as deterministic orchestration with AI used only for bounded language or classification tasks. We use agentic behavior only where that flexibility creates enough value to justify the added uncertainty.
How do we know whether an AI feature is worth building?
Start with the current workflow and baseline. A useful candidate has a defined user, repeated friction, accessible inputs, a measurable quality standard, and a safe way to handle uncertainty. We test those conditions before recommending production investment.
Do we need to choose a model provider first?
No. The task, risk, data boundary, latency, cost, and deployment requirements should shape that choice. We can compare providers against a shared evaluation and avoid coupling the product to model-specific behavior where portability has value.
Does retrieval prevent hallucinations?
No. Retrieval can provide relevant source material and improve grounding, but the generated answer can still omit, misread, or invent information. Source citations, evaluation, confidence handling, validation, and human review remain important.
Can an AI system take actions in our software?
Yes, within carefully designed boundaries. We expose narrow tools, apply the permissions of the current user, validate arguments, record audit events, make actions idempotent where possible, and require approval before consequential changes.
How do you protect private business data?
The design begins with data classification and the allowed processing boundary. We minimize what is sent, enforce tenant and user authorization, control logs and retention, review provider terms and deployment options, and test for cross-boundary leakage.
What happens when the model or provider changes?
Evaluation and observability make change manageable. Candidate updates are tested against the same workflow examples before release, while abstraction, fallbacks, versioned prompts, and staged rollout reduce avoidable surprises.
Bring one AI opportunity that already matters
Show us the workflow, the information available, and the decision or action you want to improve. We will help you test whether AI belongs there and what responsible production delivery requires.