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Solutions · 9 capabilities

Agents, assistants, search and analytics that work with your data.

Nine ways we apply AI inside real business software, each built with evaluation, permissions and a clear route for human review.

Discuss a solutionSee product concepts

01

AI agents

Software that pursues a goal by choosing and calling tools, within limits you set.

Problems it solves

  • Repetitive multi-step tasks that need judgement
  • Work that spans several internal systems

Example use cases

  • Order and refund assistant
  • IT helpdesk triage

Core features

  • Tool calling with typed schemas
  • Budgets, timeouts and retries
  • Persistent memory
  • Approval gates for risky actions

Typical technologies

  • Python
  • LangGraph
  • MCP
  • PostgreSQL

Implementation note: Define what the agent may do unsupervised before building, and log every action.

02

Multi-agent systems

Specialist agents that plan, execute and review work together under an orchestrator.

Problems it solves

  • Tasks too broad for one prompt
  • Quality problems that need an independent reviewer

Example use cases

  • Research and report drafting
  • Code change pipelines with review

Core features

  • Planner, worker and reviewer roles
  • Shared state and handoffs
  • Failure isolation
  • Cost and step limits

Typical technologies

  • LangGraph
  • Python
  • Queues
  • Tracing

Implementation note: More agents add cost and failure modes, so start with the smallest team that solves the task.

03

RAG applications

Applications that retrieve relevant content first, then generate answers grounded in it.

Problems it solves

  • Answers that ignore your latest documents
  • Hallucinated facts in customer-facing text

Example use cases

  • Policy question answering
  • Product documentation assistant

Core features

  • Ingestion pipelines
  • Hybrid retrieval and reranking
  • Citations
  • Refusal when evidence is thin

Typical technologies

  • pgvector
  • Elasticsearch
  • FastAPI
  • Reranker models

Implementation note: Retrieval quality drives answer quality, so measure it separately from the model.

04

Enterprise AI assistants

Assistants wired into company systems and identity, respecting who can see what.

Problems it solves

  • Staff losing time searching across tools
  • Generic chatbots with no access to real data

Example use cases

  • Sales enablement assistant
  • Internal HR and IT assistant

Core features

  • SSO and role-aware answers
  • Connectors to wikis, tickets and CRM
  • Audit logs
  • Admin controls

Typical technologies

  • OIDC
  • MCP
  • Next.js
  • PostgreSQL

Implementation note: Permissions must be enforced at retrieval time, not only in the interface.

06

AI coding tools

Assistants and reviewers that learn your repositories and conventions.

Problems it solves

  • Slow code review cycles
  • Inconsistent practices across teams

Example use cases

  • Automated first-pass code review
  • Migration assistants

Core features

  • Repository-aware suggestions
  • Pull request review
  • Test generation
  • Policy checks

Typical technologies

  • GitHub APIs
  • LLM APIs
  • Static analysis
  • CI hooks

Implementation note: Keep engineers as the final approvers and track the quality of accepted suggestions.

07

Voice AI

Phone and in-app voice agents that listen, reason and act through your systems.

Problems it solves

  • Missed calls and long hold times
  • Routine phone tasks tying up staff

Example use cases

  • Appointment booking line
  • After-hours support

Core features

  • Speech recognition and synthesis
  • Barge-in handling
  • Calendar and CRM tools
  • Human handoff

Typical technologies

  • Streaming STT/TTS
  • WebRTC
  • Telephony APIs
  • LLM APIs

Implementation note: Latency matters more than polish, and callers must always be able to reach a person.

08

Multimodal AI

Systems that read images, documents and audio alongside text.

Problems it solves

  • Manual data entry from forms and photos
  • Unstructured media nobody can search

Example use cases

  • Invoice processing
  • Inspection photo analysis

Core features

  • Document and image extraction
  • Structured output validation
  • Confidence scoring
  • Review queues

Typical technologies

  • Vision-capable LLMs
  • OCR
  • Python
  • Queues

Implementation note: Always validate extracted fields against business rules and route low confidence to people.

09

AI analytics

Natural-language access and automated insight on top of governed business data.

Problems it solves

  • Analysts as a bottleneck for simple questions
  • Reports nobody reads

Example use cases

  • Self-service sales questions
  • Weekly executive summaries

Core features

  • Text-to-query on a semantic layer
  • Anomaly alerts
  • Narrated dashboards
  • Query audit

Typical technologies

  • dbt
  • PostgreSQL
  • BigQuery
  • LLM APIs

Implementation note: Grounding in a governed metrics layer prevents confident but wrong numbers.

Projects

Reference projects that show this in practice.

Systems we designed as demonstrations, using sample data. Each has features, architecture and security notes.

See all projects

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