AI Development
Practical AI systems for workflows, knowledge, support, and automation
Build useful AI features with clear data boundaries, evaluation criteria, security controls, and human review where needed.
What we build
Specific outputs matched to the project goal.
Service outcome guidanceIdeal for teams that need controlled AI features inside support, operations, knowledge management, sales, or ecommerce workflows.
LLM integrations
RAG systems
AI chatbots
Internal knowledge assistants
Workflow automation
Model evaluation
Data privacy and governance
Human-in-the-loop review
AI maintenance and monitoring
Deliverables
Concrete work products, not vague capability claims.
Use-case brief with success criteria, risks, and human review points
Customer support chatbot, document search assistant, lead qualification assistant, internal SOP assistant, or ecommerce product recommendation assistant
RAG architecture, prompts, retrieval rules, access controls, and evaluation set
LLM/API integration with logging, monitoring, fallback behavior, and escalation paths
Data privacy, governance, maintenance, and improvement plan
Technology stack
Tools selected for maintainability and fit.
OpenAIAnthropicLangChainLlamaIndexPythonVector DBsPostgreSQLCloud APIs
- Timeline range
- 3-8 weeks for prototypes; production systems depend on integrations and governance needs.
- Example engagement
- A RAG support assistant prototype with source inventory, retrieval rules, evaluation questions, human handoff, and improvement backlog.
- Proof artifact
- Workflow diagram, source inventory, evaluation set, risk notes, and demo transcript.
Development process
A clear path from planning to support.
The exact scope changes by project, but the delivery rhythm keeps decisions, QA, and launch readiness visible.
- 1Select practical use case
- 2Map data boundaries and access rules
- 3Prototype retrieval or workflow
- 4Evaluate responses and edge cases
- 5Add security controls and human review
- 6Deploy, monitor, and improve
Governance, evaluation, and privacy approach
Built around measurable delivery quality.
Define what the AI system can access, what it must refuse, and when it should hand off to a person.
Create evaluation examples for accuracy, retrieval quality, tone, safety, and workflow completion.
Log prompts, retrieved sources, user feedback, and failures so improvements are evidence-based.
Review privacy, permissions, retention, and vendor constraints before production deployment.
Maintenance and support
Support after the first launch.
Prompt, retrieval, and evaluation tuning
Monitoring for failures, stale content, and workflow gaps
Governance updates as data sources, users, and policies change
Quality checklist
What we verify before handover.
The checklist changes by scope, but these are the checks expected for this service.
Use-case boundary, refusal rules, escalation, and data retention reviewed
Evaluation set for accuracy, source coverage, safety, and handoff
Logging, access control, prompt/retrieval versioning, and monitoring
Human review workflow for sensitive or uncertain outputs
Related services
Useful internal links for planning the full scope.
FAQs
Common questions before starting.
What makes an AI use case worth building?
A good use case has clear users, source data, success criteria, risk boundaries, and a workflow where AI output can be checked or corrected.
Can you build RAG systems over our internal documents?
Yes. We can connect approved document sources, structure retrieval, add citations or source references, and evaluate answer quality before launch.
How do you reduce AI risk?
We use access controls, refusal rules, evaluation sets, logging, fallback behavior, and human review for workflows where incorrect output could create harm or confusion.
Do you maintain AI systems after launch?
Yes. AI systems need monitoring, content updates, prompt and retrieval tuning, model/vendor review, and evaluation as usage patterns change.
How much does an AI prototype or production system cost?
Cost depends on data sources, retrieval complexity, model/API usage, integrations, evaluation requirements, security controls, and support needs. We often recommend a scoped prototype before a larger rollout.
How long does an AI project take?
A narrow prototype may move quickly when source data is ready. Production systems need more time for data permissions, evaluation sets, logging, security, workflow integration, and human review.
AI Development
Ready to turn the scope into a practical plan?
Share the goal, timeline, current stack, and constraints. We will recommend the next useful step.