About Us

Boolean Solutions is a trusted technology partner for SaaS companies and startups, delivering end-to-end web and mobile development, AI, and product consulting that turns bold ideas into scalable digital products.

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AGENTIC AI & LLM ENGINEERING

Agents, RAG and LLM features that survive contact with real users — measured, not guessed.

Agentic AI

Agents that work on a Tuesday, not just in the demo

The hard part of AI is not calling a model. It is knowing whether the answer was right, catching it when it drifts, and keeping cost and latency inside something a business can live with. We build agentic systems with that measurement baked in from the first sprint.

  • Evals before scaleWe define how you will know it worked before we grow the pipeline
  • Grounded in your dataRetrieval over your documents, with citations you can check
  • Traced end to endEvery call, token and tool hop visible in production

How we architect an agentic system

Five layers, each with its own failure modes. Most AI projects stall because one of them was treated as an afterthought — usually evaluation or retrieval quality.

Interface

Chat, copilot, batch job or a plain API. Streaming responses, cancellation, and a human-in-the-loop approval step wherever a wrong action would be expensive.

Orchestration

Planning, tool calling, state and hand-off between agents. Explicit graphs over implicit prompt chains, so behaviour is inspectable and testable.

Retrieval

Ingestion, chunking, embeddings, hybrid search and re-ranking. Agentic RAG where the agent decides what to look up rather than stuffing one query into context.

Model

Model selection and routing, structured outputs, fallbacks when a provider degrades, and cost controls that keep a runaway loop from becoming an invoice.

Observability

Tracing, eval suites run in CI, regression alerts on prompt changes, plus latency and token dashboards. This is what turns a prototype into something you can change safely.

The AI stack we work in

We have shipped agentic features inside Rails, Node and Python codebases, so the AI layer fits the application you already have rather than arriving as a separate Python service nobody wants to maintain.

Capabilities

The patterns we build with, chosen per problem — a single well-grounded RAG call often beats a crowd of agents.

  • ARAgentic RAG
  • MAMulti-agent systems
  • TCTool calling
  • SOStructured outputs
  • HIHuman in the loop
  • GGuardrails

Orchestration frameworks

Default: LangGraph when control flow matters, because an explicit state graph is far easier to debug and evaluate than a chain of prompts.

  • LangGraph
  • LangChain
  • CCrewAI
  • LLlamaIndex

Ruby & Rails AI

Rails shops rarely want a bolt-on Python service. We build agents natively in Ruby using RubyLLM and Langchain.rb, and have worked with the AI Agents SDK that came out of Chatwoot.

  • RubyLLM
  • Langchain.rb
  • Chatwoot AI Agents SDK
  • Ruby on Rails

Python, Node & BEAM

Python where the ecosystem is richest, Node when the agent sits next to an existing JS backend, and Jido on Elixir when you want the BEAM’s supervision model behind long-running agents.

  • Python
  • LangGraph (Py)
  • CCrewAI
  • Node.js
  • LangGraph (JS)
  • Jido
  • Elixir

Models & serving

Provider-agnostic by design. We route per task and keep a fallback, so a rate limit or a deprecated model is a config change rather than an outage. Self-hosted via Ollama where data cannot leave your estate.

  • OpenAI
  • Anthropic Claude
  • Hugging Face
  • Ollama
  • PyTorch

Retrieval & memory

Default: pgvector — if your data already lives in PostgreSQL, one less system to run is usually worth more than a marginal recall improvement.

  • pgvector
  • Qdrant
  • Elasticsearch
  • Redis

Evals & LLMOps

Default: Langfuse for tracing and eval runs. Evals live in CI next to the unit tests, so a prompt change that quietly degrades quality fails the build.

  • LLangfuse
  • ESEval suites
  • PVPrompt versioning
  • TTracing
  • CTCost & token monitoring

AI-assisted delivery

We use agentic coding tools ourselves, under the same rules we apply to any contributor: every change reviewed by a human, every change covered by tests.

  • Claude Code
  • CCursor
  • ACAI code review

Our bias. Start with the simplest thing that could work — often retrieval plus one good prompt — and only add agents when a task genuinely needs planning or tool use. Complexity in an AI system is paid for twice: once in latency, once in debugging.

What we build

Knowledge assistants

Answer questions over your documents, tickets and wikis with citations, so users can verify rather than trust blindly.

Workflow agents

Multi-step automation that reads, decides and acts across your systems, pausing for a human where the stakes justify it.

AI features in existing products

Summaries, drafting, classification and search added to a live application without destabilising what already works.

AI rescue & hardening

A prototype that impressed in a demo but misbehaves in production. We add evals, tracing and guardrails until it is trustworthy.

How we keep quality measurable

AI features fail quietly. These practices are what make the difference between shipping once and being able to keep changing it.

  • An eval set built from real examples before the pipeline grows
  • Evals run in CI so prompt regressions fail the build
  • Full tracing on every call, tool hop and retrieval
  • Token and latency budgets with alerts when they slip
  • Prompt-injection and jailbreak defences on untrusted input
  • Human approval gates on any irreversible action
How We Build

The Agentic Engineering Cycle

We understand the AI industry and build every essential component of your solution — at every stage. Our loop pairs AI speed with human judgment, so you ship with confidence, not guesswork.

01

Design & Prototype

Shape UX/UI and validate fast with Figma & Figma Make.

02

Vibe-Code Build

Build at speed with Claude Code and AI pair-programming.

03

Automated Testing

CI/CD pipelines run automated tests to stop regressions.

04

AI Code Review

AI reviews every change for quality, security & standards.

05

Human-in-the-Loop

Senior engineers verify AI output and own the outcome.

Quality gate
06

Confident Deploy

Release with evals & observability watching in production.

Human-in-the-loop, by design. AI accelerates every step of the loop — but our engineers review and sign off before anything ships, so you get AI speed with the confidence of human quality, all the way to deployment.