AI & Automation
AI that does real work.
We add AI where it earns its keep — LLM features, retrieval pipelines, and automation wired into your actual operations. FortifiLab builds AI integrations connected to real data and workflows, with evaluations so you can trust the output.
Grounded in your data
RAG pipelines connect models to your real content so answers are accurate and useful, not generic.
Wired into operations
We connect AI to the tools your team already uses — Slack, CRMs, internal apps — so it saves real time.
Measured, not magic
Evaluations and guardrails keep outputs reliable, so AI features hold up in production.
What you get
- LLM & chatbot features
- RAG & knowledge-base pipelines
- Workflow automation
- AI agents & copilots
- Evaluations & guardrails
- Integration with existing apps
Stack
How we work
A clear, low-risk process
Use-case scoping
We identify where AI actually saves time or money, and rule out where it does not, before committing to a build.
Data & retrieval
We connect models to your real content with a retrieval pipeline so answers are grounded, not generic.
Build & evaluate
We ship the feature with evaluations and guardrails so behavior is measured and predictable, not a demo that breaks in production.
Integrate & monitor
The feature is wired into the tools your team already uses, with monitoring to catch drift and cost.
Selected work
Proof, not promises
Videxa — AI SaaS
Video intelligence SaaS with a RAG pipeline and transcription orchestration. 90% faster review.
CCA User Insight Hub — RAG
Retrieval-augmented chatbot on LangChain + OpenAI with a custom knowledge ingestion pipeline.
AI Automation Pipeline
Zoom → AI → Slack pipeline that extracts structured insights from recordings automatically.
See all case studies.
Frequently asked
Can you add AI to my existing product?
Yes — most of our AI work is integrating LLM features and automation into software that already exists, not building from scratch.
How do you keep AI outputs reliable?
We ground models in your data with retrieval, add guardrails, and run evaluations so behavior is measurable and predictable.
What can AI automation actually do for my business?
Common wins include support copilots, document and knowledge search, content pipelines, and automating repetitive multi-step workflows.
Which AI models and tools do you use?
We are model-agnostic and choose per use case — commonly OpenAI models with LangChain, a vector database for retrieval, and Python/FastAPI services, integrated into your existing stack.
How do you control AI costs?
We right-size models, cache where possible, and add monitoring on token usage so spend stays predictable as usage grows.
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