ABOUT CT LABS

CT Labs builds AI systems that teams can run in production

CT Labs is an AI consulting and delivery team focused on practical workflows. We help organizations define the proper scope, build AI workflows and agentic workflows, and execute AI deployment and production rollouts with evaluation, governance, and operational readiness.

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COMPARISON

Why CT Labs Stands Out

CT Labs compared with the Big 4 on execution speed, governance, and measurable ROI.

✓4× faster to ROI through deployment focused sprints
✓Production build team from day one
✓Agentic workflows with governance built in
✓KPI baselines and ROI tracking from kickoff
✓30+ prebuilt agents ready to plug into production grade systems, delivering value on accelerated timelines

Big 4 Consulting

✕Slower ROI driven by multi-phase programs
✕Larger teams and heavier coordination
✕Governance delivered as parallel workstreams
✕KPI reporting starts later in the cycle
✕Scale depends on expanded scope and change programs

HOW WE WORK

Delivery approach

Small senior team

Engagements are led by experienced practitioners, with clear ownership and direct communication.

Clear scope and acceptance criteria

We define what success looks like, how it will be measured, and what is included in delivery.

Fast feedback cycles

We build in short iterations with visible progress and stakeholder reviews.

Integration and handoff

We deliver documentation, playbooks, and training materials so internal teams can operate confidently.

DIFFERENTIATORS

What makes CT Labs different

Delivery anchored in evaluation

Every engagement includes metrics, test sets, and a regression plan so performance remains stable.

Agentic workflows designed for control

We build agentic workflows with permissions, approvals, audit logs, and risk-aligned escalation paths.

Multi-model consulting for cost and performance

Routing strategies help teams balance quality, latency, and spend across tasks.

Multi-modal consulting for real document workflows

We support text and vision pipelines with validation and measurable accuracy.

CLIENTS

Who we work with

CT Labs supports organizations that want reliable AI delivery. Typical stakeholders include CEOs, COOs, CTOs, Heads of Engineering, product leaders, and operations teams.

Customer operations workflows connected to ticketing and knowledge systems

Internal knowledge workflows across docs and tools

Document intake and processing supported by multi-modal consulting

Sales and revenue workflows connected to CRM and communication tools

Agentic workflows for research, approvals, and multi-step execution

FAQs

Frequently asked questions

What is CT Labs' AI assessment and what deliverables will I receive?

CT Labs' AI assessment is a sprint that converts business workflows into technical scope, evaluation criteria, and a delivery roadmap. It works with your team to identify impactful workflows, necessary data sources, the model approach balancing quality and cost, and controls for security and governance. Outputs include a report on prioritized use cases, a target architecture with integration details, an evaluation plan with metrics, and a delivery roadmap.

How does CT Labs design LLM-powered workflows and ensure they are reliable?

CT Labs' LLM consulting covers workflow design, retrieval, context strategy, orchestration, tool integration, evaluation, routing, cost control, and multi-modal consulting. It helps clients decide between large language models (LLMs) and traditional machine learning, with LLMs suited for natural language tasks and ML models for structured data. Often, they combine both: LLMs handle unstructured input, and ML models manage structured decisions. Reliability is maintained through evaluation, model routing, and governance, such as audit logs and policies.

Which organisations benefit most from CT Labs' agentic workflows and multi-model consulting?

Agentic workflows suit multi-step operations like ticket triage, research, and approvals, especially when approvals involve diverse inputs or system updates. CT Labs helps balance quality, latency, and cost by routing tasks across models. Typical uses include customer support, sales, knowledge management, document processing, and engineering tasks. Benefits include automating workflows, cutting costs, enhancing quality, and increasing revenue.

How does CT Labs address data security, governance, and compliance in AI projects?

CT Labs emphasizes governance and risk in every project, recommending early planning for security and compliance. This includes protecting training data from misuse, using secure infrastructure and encryption, and offering on-premises deployment for sensitive data. They also implement policy checks, approval steps, and audit trails to meet internal and regulatory standards. Continuous evaluation frameworks ensure ongoing performance, regression detection, and compliance.

How do you measure the success of an AI deployment or LLM project with CT Labs?

Success is measured by technical and business metrics. CT Labs sets clear KPIs during assessment, aligning with business goals. For generative AI, focus on efficiency (time saved or labor costs), revenue impact (conversions or new offerings), and risk reduction (compliance or response times). Evaluation plans include metrics for accuracy, latency, and cost; monitoring tracks error rates and performance; adoption metrics measure engagement. CT Labs recommends a phased rollout: launch a pilot or MVP in 6–12 weeks to demonstrate value, start with low-risk use cases for quick wins, then expand based on results.

GET STARTED

Build with a team that ships and supports production outcomes

Share the workflow, systems involved, and timeline. CT Labs replies with a scoped next step and an outline of the delivery plan.

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