AI AUTOMATION
Put AI to work on real business tasks
We build AI-powered workflows and applications that connect language models, external APIs and your existing systems. From repetitive operational tasks to voice agents that handle structured interactions, our focus is on practical automation with defined responsibilities, appropriate safeguards and performance that can be evaluated.
How it works
From a manual workflow to an engineered system
Identify the right workflow
We examine the task, its inputs and expected outcomes to determine whether AI automation is appropriate and where human involvement should remain.
The task, its inputs and outcomes, and where a person stays involved.
Map tools, data and permissions
We define the systems the workflow can access, which actions it may perform and what information or approvals it requires.
What the workflow may access, what it may do and what needs approval.
Build and evaluate
We implement the automation or agent, then evaluate its behaviour against agreed scenarios, including relevant edge cases and failure conditions.
An agent evaluated against agreed scenarios, edge cases and failures.
Instrument and refine
We measure the relevant stages of execution, including latency and provider usage where applicable, to guide technical improvements and operational decisions.
Each stage measured, so improvements follow observed performance.
What’s included
AI systems designed around actual workflows
Turn repetitive tasks into workflows that do the work.
Manual steps replaced by pipelines and agents.
- 01
Workflow automation
Replace selected repetitive steps with structured pipelines that connect the applications and services your team already uses.
- 02
AI and API integration
Connect language models and relevant external APIs to existing software, data sources and business processes.
- 03
Agentic tool-calling
Build agents that can retrieve information or perform specifically defined actions through approved tools, with appropriate checks around what they may do.
- 04
Real-time voice applications
Implement speech-to-text, model orchestration and text-to-speech pipelines for voice interactions, including consideration of latency and interruption handling.
- 05
Model and provider evaluation
Compare suitable model and speech providers against the requirements of your application, such as response time, language support and operating cost.
- 06
Instrumentation and testing
Record relevant execution stages and evaluate system behaviour so technical improvements can be based on observed performance rather than assumptions.
FAQ
Common questions
Can an AI workflow connect to our existing software?
It can, where suitable APIs or integration methods are available. We assess the systems involved and define the permissions, data access and actions required before implementation.
Can a voice agent support Urdu, Hindi and English?
Potentially, depending on the selected speech and AI providers and the requirements of the interaction. We evaluate providers against the languages and speaking patterns expected in the intended use case, including Urdu, Hindi and English code-switching where relevant.
Can an AI agent make decisions and take actions automatically?
We define the actions an agent is permitted to take and the checks required around those actions. Sensitive or consequential workflows may need explicit approvals, human review or escalation paths instead of unrestricted automation.
How do you measure the performance of an AI workflow?
The relevant measures depend on the application. They may include response latency, task success, error rates, language quality, usage and model-provider cost. We establish appropriate evaluation criteria during scoping.
Are AI model and voice-provider costs included?
Usage-based AI services, speech providers and other external APIs may incur separate fees. Applicable third-party costs are identified in the quotation.