Voice agents & conversational systems
Speech-to-text, dialogue logic and text-to-speech chained into an agent that runs a real conversation, not a scripted menu.
Manzar Labs ▸ AI Development · L/03
AI that ships as a feature, not a slide.
Chat that knows your business, voice agents that hold a real conversation, and a guardrail for the day the model is wrong.
What we actually do
Seven ways models earn their place in production
Not a lab. Every engagement below ends with the model inside your product, behind guardrails, with its receipts showing.
Grounded answers
01
AI chat & copilots
Chat for your customers and copilots for your staff, grounded in your own databases and documents, so every answer cites the record it came from.
Held to account
02
LLM-backed features
Retrieval-augmented generation wired into an existing product, grounded in your data, not the open internet.
Workflow automation
The repetitive steps between two systems handed to an agent that runs unattended and logs what it did.
Model evaluation & guardrails
Prompt testing, output validation and fallback paths, so a bad response gets caught before a customer sees it.
Fine-tuning & prompt engineering
Tuning a model's behaviour to your product's voice and edge cases, not the default assistant tone.
AI infrastructure
Vector databases, embedding pipelines and inference cost kept under control at production volume.
Up close
Watch an eval run.
Before an agent touches your customers, it runs the gauntlet: real prompts from your logs, checked answers, cited sources, guardrails that catch the bad day. This is the boring machinery that separates a demo from a feature.
How it ships
Discover, build, ship
Discover
We map the workflow the model has to fit into, and the failure modes that would make it worse than doing the task by hand.
Build
Prompt and pipeline iteration against real inputs from your product, evaluated against cases that actually happen, not a curated demo set.
Ship
The system goes live behind guardrails and logging, with a plan already written for the day it gets something wrong.
The stack
Python, LLM APIs (OpenAI, Anthropic, and open-weight models where the job calls for it), retrieval and orchestration tooling, vector databases such as pgvector or Pinecone, speech pipelines for voice agents. What we reach for, chosen per job. Never a badge wall.
PythonOpenAIAnthropicOpen-weight modelspgvectorPineconeLangGraphSpeech pipelinesEvals
Ardenta · AI front desk
"Ardenta answers the phone around the clock, replies from the business's own documents with the source it used, and books straight into the calendar, on the number they already have."
Join the Ardenta waitlist
Pricing
See pricing ↗
AI features are usually scoped as a two-week Sprint first, to prove the approach before it's built into the product. See how engagements are typically priced.
