Skip to content
MAZAI.Labs

Applied AI engineering · Brand architecture

AI systems engineered for production.

MAZAI. Labs specifies, builds and operates AI in live business workflows — strategy, autonomous agents, conversational and voice interfaces — with the evaluation, observability and brand architecture required to run them at scale.

Request a technical scoping call→View services

Technical response within one business day

01

AI Strategy & Solutions

Workflow decomposition and data-readiness assessment

02

Autonomous AI Agents

Agent orchestration, tool schemas and state design

03

Conversational AI

Retrieval pipeline, chunking and grounding strategy

04

AI Voice Automation

Speech-to-text, synthesis and latency-budget engineering

Agent orchestrationRetrieval-augmented generationSpeech-to-text & synthesisEvaluation harnessesTool & function callingWorkflow automationModel routing & fallbackObservability & tracingPrompt-injection defenceAI governanceDesign token systemsCore Web Vitals engineeringAgent orchestrationRetrieval-augmented generationSpeech-to-text & synthesisEvaluation harnessesTool & function callingWorkflow automationModel routing & fallbackObservability & tracingPrompt-injection defenceAI governanceDesign token systemsCore Web Vitals engineering

Services

Four core systems.
Engineered, not prototyped.

Each service is independently scoped, specified against a measurable outcome, and validated by an evaluation harness before it reaches production.

01

AI Strategy & Solutions

Systems architecture and a costed deployment roadmap, derived from your operational data rather than from market narrative.

We model your workflows end to end, quantify unit economics per process, and identify where inference genuinely outperforms deterministic software. The output is a reference architecture, a build-versus-buy position per component, and a sequenced roadmap with projected cost, latency and accuracy envelopes.

  • Workflow decomposition and data-readiness assessment
  • Reference architecture and model selection rationale
  • Unit-economics model: cost per task, per seat, per call
  • Sequenced roadmap with success metrics per phase
Enquire about AI Strategy & Solutions→
02

Autonomous AI Agents

Tool-using agents that execute multi-step operational workflows under explicit constraints, logging and human escalation.

We engineer agents with scoped tool access, structured state, deterministic fallbacks and bounded retries — then validate them against a versioned evaluation suite before release. Every run is traced end to end, with token and cost ceilings enforced at the orchestration layer and defined handoff thresholds to a human operator.

  • Agent orchestration, tool schemas and state design
  • Retrieval and knowledge-base integration
  • Evaluation harness, regression suite and guardrails
  • Tracing, cost controls and escalation policy
Enquire about Autonomous AI Agents→
03

Conversational AI

Grounded assistants for support, sales and internal knowledge — measured on resolution rate, not on transcript vibes.

Retrieval-grounded conversational systems built on your documentation, product data and ticket history, with citation enforcement and explicit refusal behaviour on low-confidence retrieval. Deployed to web, in-product and messaging surfaces, instrumented for containment rate, deflection, latency and CSAT from day one.

  • Retrieval pipeline, chunking and grounding strategy
  • Dialogue policy, tone calibration and refusal handling
  • Web, in-product and messaging channel integration
  • Containment, deflection and quality instrumentation
Enquire about Conversational AI→
04

AI Voice Automation

Low-latency voice agents for inbound and outbound telephony, engineered around turn-taking and interruption, not scripts.

Real-time speech pipelines tuned for sub-second response, with barge-in handling, endpointing, and graceful degradation to a human queue. Integrated with your telephony stack and CRM so every call is transcribed, classified and written back to the record it belongs to, under the disclosure and consent rules of your jurisdiction.

  • Speech-to-text, synthesis and latency-budget engineering
  • Turn-taking, barge-in and endpointing behaviour
  • Telephony, CRM and calendar integration
  • Call transcription, classification and compliance logging
Enquire about AI Voice Automation→

Method

From signal
to system.

  1. 01

    Audit

    We instrument the current workflow, quantify friction and cost per step, and isolate the highest-leverage intervention points.

  2. 02

    Design

    Architecture, model selection, data flows and acceptance criteria are specified and signed off before implementation begins.

  3. 03

    Build

    Implementation against a live environment with weekly demonstrations, validated continuously by an evaluation harness.

  4. 04

    Automate

    Systems are integrated into your telephony, CRM and internal tooling so decisions execute without manual relay.

  5. 05

    Optimise

    Post-launch we track evaluation scores, latency and spend against baseline, then iterate where the data justifies it.

Principles

Engineering
standards.

AI deployments fail for unglamorous reasons: an unsuitable use case, no evaluation baseline, and no visibility once it is live. These are the controls we hold ourselves to.

  • 01

    Specified against measurable outcomes

    Every engagement opens with the metric under test — containment rate, handle time, cost per resolution, conversion. Where a deterministic system outperforms a model on that metric, we specify the deterministic system and say so in writing.

  • 02

    Evaluation before deployment

    Nothing reaches production without a versioned evaluation set and a regression baseline. Model and prompt changes are measured against that baseline, so quality movement is observable rather than anecdotal.

  • 03

    Observable and cost-bounded in production

    Request tracing, token accounting, latency percentiles and enforced spend ceilings ship as part of the system. Degradation surfaces on a dashboard before it surfaces in a customer complaint.

  • 04

    Single team across system and identity

    Engineering and brand are specified against one strategy rather than negotiated across two vendors. Design tokens map to implementation, and the product's behaviour and its positioning stay coherent.

  • 05

    Full transfer of ownership

    Source code, prompts, evaluation sets, infrastructure configuration and design files transfer to you on delivery, with documentation sufficient for your team to operate the system unaided.

FAQ

Technical
questions.

What engineering and procurement teams ask on the first scoping call.

How do you determine whether a use case warrants a model at all?

We benchmark it against the deterministic alternative. If rules, search or a conventional integration meet the accuracy and cost target, that is what we specify — inference introduces variance, latency and per-request cost, and it should only be used where it demonstrably earns them.

How is quality measured before something goes live?

Each system ships with a versioned evaluation set built from your real cases, plus a regression baseline. Releases are gated on that baseline, and the same harness runs post-deployment so drift is detected against a known reference rather than by inspection.

What latency is achievable for voice automation?

We engineer to a sub-second perceived response budget, allocated explicitly across transcription, inference and synthesis. Achieved figures depend on your telephony path and model selection, and we report measured percentiles rather than best-case numbers.

Can we engage you for a single capability?

Yes. Each capability is independently scoped. Clients frequently begin with an audit or strategy engagement and use its findings to determine what, if anything, is built subsequently.

How is work priced?

Fixed price per phase, quoted after a technical scoping call. Audit and strategy engagements are a flat fee. Managed operations are a monthly retainer with a defined response SLA, terminable at any time.

How are data residency and security handled?

We deploy within your infrastructure and provider accounts wherever feasible, specify retention and residency explicitly in scope, and execute an NDA before material is exchanged. Prompt-injection and data-exfiltration testing is included in every audit.

Who retains ownership of the delivered system?

You do, in full, on delivery — source code, prompts, evaluation sets, infrastructure configuration and source design files. There is no proprietary runtime and no licensing dependency on us.

Start a conversation

Define the problem.
We will scope the system.

A technical scoping call, not a sales presentation. Send the brief and you will get back an architectural position, an indicative cost envelope and a straight answer on feasibility.

Response time
Technical response within one business day
What are you looking for?
Estimated budget

We reply within one business day. No newsletter, no sales sequence.