Fragmented context
Answers depend on information spread across tickets, documentation, CRM records and internal systems.
AI SUPPORT AUTOMATION FOR B2B SAAS
Automate repetitive customer support without losing customer context.
Draya AI SupportOps connects your helpdesk, knowledge base, CRM and product APIs to deliver grounded answers, execute approved workflows and hand complex cases to humans with complete context.
Designed for B2B SaaS, developer tools, AI products and cloud platforms.
SupportOps control layerevaluated
Helpdesk Knowledge CRM Product API
Grounded resolutionEvidence verified
Customer, account and product context assembled before a response or action is approved.
Approved answer Human handoff
Where simple bots break down
Most support AI fails when company context becomes complicated.
A knowledge-base answer is only one part of a real support case. Reliable automation must understand the customer, product state, account permissions, previous conversations and the point where a human should take over.
Live product data
Many cases require current account, subscription, order or usage data—not another static FAQ response.
Unsafe automation
An AI agent needs confidence thresholds, approved actions, audit trails and reliable human escalation.
Controlled support flow
A grounded path from intake to human handoff.
Each case moves through approved context, answer generation and a confidence check before the workflow responds or escalates.
- Intake
- Context
- Answer
- Confidence
- Human handoff
Keep the tools. Add the intelligence.
We do not replace your helpdesk. We make it context-aware.
Keep the support tools your team already uses. Draya AI Lab designs and implements the retrieval, workflow, safety and evaluation layer needed to automate repetitive work responsibly.
Draya AI SupportOps
What we implement
A focused layer that brings approved context, controlled action and measurable quality into the support workflow you already have.
01
Agent Assist
Draft grounded responses with citations and relevant customer context for human approval.
02
Verified Resolution
Automatically answer suitable repetitive questions only when evidence and confidence requirements are met.
03
Approved API Actions
Execute scoped workflows such as checking account state, updating an allowed field or triggering an approved operational step.
04
Human Handoff
Escalate uncertainty, exceptions and high-risk requests with a complete summary and supporting evidence.
05
Evaluation and Knowledge Ops
Monitor answer quality, retrieval failures, deflection quality and documentation gaps over time.
A controlled path to production
From support bottleneck to controlled automation
Begin with one measurable workflow, test its failure modes and expand only when the evidence supports it.
- 01
Audit
Review ticket categories, volume, knowledge sources, systems, security constraints and escalation rules.
- 02
Prioritize
Select high-volume, low-risk workflows with measurable success criteria.
- 03
Build the pilot
Connect approved data sources, retrieval, tools and human handoffs.
- 04
Evaluate
Test against representative support cases, failure modes and adversarial inputs before expansion.
- 05
Operate and improve
Monitor production quality, costs, latency, documentation gaps and new automation candidates.
Ideal customer profile
Best suited for support-heavy technical products
The strongest fit is a B2B SaaS, API, developer-tool, AI or cloud company with recurring technical questions, an existing helpdesk and enough support volume to measure operational impact.
Earlier-stage? Very early products with low support volume are usually better served by improving documentation and helpdesk workflows before investing in custom AI automation.
Good fit
- Approximately 5–50 employees
- An established helpdesk and knowledge base
- Roughly 1,000+ monthly conversations, or fewer high-value technical cases
- Repetitive Tier-1 questions or manual account lookups
- Human review retained for uncertainty and risk
Safety by design
Automation with boundaries
Useful automation is not unrestricted autonomy. Each workflow is scoped around explicit access, evidence, escalation rules and ongoing review.
- 01
Approved knowledge sources only
- 02
Role-based access and least-privilege tool permissions
- 03
Citations or evidence for grounded answers where appropriate
- 04
Confidence-based escalation
- 05
Human approval for sensitive or irreversible actions
- 06
Audit logs, evaluation datasets and ongoing quality review
- 07
Protection against prompt injection and unauthorized data access
Clear starting points
Start with one measurable support workflow
Every engagement begins with defined evidence, risk boundaries and a practical measure of success.
Support Automation Audit
from $750
- Ticket and workflow analysis
- Knowledge and integration review
- Prioritized automation roadmap
- Risk and measurement plan
Typical delivery: 5–7 business days
Start with an auditRecommended starting point
Focused Pilot
from $3,000
- One prioritized support workflow
- Approved knowledge and system connections
- Human handoff and safety controls
- Evaluation set and launch report
Typical delivery: 2–4 weeks
Start with an auditManaged Optimization
from $750/month
- Quality and failure monitoring
- Retrieval and prompt improvements
- Evaluation maintenance
- Knowledge-gap reporting
- Monthly optimization review
Ongoing monthly engagement
Start with an auditFinal scope and pricing depend on support volume, integrations, data sensitivity and workflow complexity.
Early pilot programme
Accepting a limited number of early pilot partners
We are working with selected technical-product teams that want to automate one measurable support workflow without committing to a large platform migration.
About Draya AI Lab
Engineering-led AI support automation
Draya AI Lab is an engineering-led AI studio focused on dependable support automation for technical products. We combine AI retrieval and agent workflows with cloud infrastructure, production support practices, observability and human escalation design.
Draya AI Lab is a brand operated by Draya Multidimensional Industries Private Limited, India.
Questions, answered
What technical support teams ask first
Clear boundaries make for better automation decisions.
No. The service targets repetitive work and agent assistance while escalating uncertainty and sensitive cases to humans.
Usually not. The engagement begins by assessing the existing helpdesk and available integration options.
Not by default. Most implementations ground model responses in approved knowledge and scoped live systems. Any fine-tuning requirement must be separately evaluated and approved.
We use controlled retrieval, evidence, evaluation datasets, confidence thresholds, restricted tools and human handoff. No AI system can guarantee zero errors, so the workflow is designed around safe failure.
Start with a frequent, measurable and low-risk workflow that currently consumes meaningful support time.
A focused pilot typically takes 2–4 weeks after access, scope and success criteria are agreed.
Start with clarity
Find the first support workflow worth automating.
Start with a focused audit of your ticket patterns, knowledge sources, integrations and operational risks.