Enterprise AI · AI Automation · Since 2013
Quest GLT builds AI systems that live inside your real workflows — drafting, checking, routing, and logging — so your people only touch the decisions that need human judgment. We've been shipping enterprise software for Fortune 500 clients since 2013. AI is the newest tool in the box, not the first.
What we build
Workflows that complete multi-step processes end to end
Content engines with compliance guardrails baked in
Trained on your data, not the open internet
Flags risk before it costs you
Across Veeva, Salesforce, SAP, Oracle & more
EST. 2013 · USA · UAE · CANADA · INDIA
AI services
Every engagement starts with the same question: which repetitive, expensive, error-prone process should stop being manual first? Then we build for that — not for a slide deck.
AI agents that handle multi-step work — draft, validate, route, log — across your existing systems. Humans approve; agents execute. This is the architecture behind our Teva engagement below.
For: ops-heavy teams drowning in handoffsContent engines that draft emails, documents, slides, and training material from your approved libraries — contextualized by region, segment, or product line, with version control built in.
For: marketing & medical content at scaleAutomated pre-checks for prohibited terms, policy violations, and missing references — plus audit trails that log every change for regulators. Built for FDA, EMA, and GDPR environments.
For: regulated industries — pharma, finance, healthAssistants trained on your knowledge base for HCP query management, internal support, and customer service — with escalation rules so the hard questions still reach a human.
For: support teams & field-facing rolesModels that anticipate compliance risk before submission, forecast engagement, and surface what your dashboards currently tell you three weeks too late.
For: leadership teams who hate surprisesThe unglamorous part that makes everything else work: connecting AI with Veeva, Salesforce, SAP, Oracle, SharePoint, Okta, Power BI, and Tableau so nothing waits on a copy-paste.
For: CIOs tired of disconnected pilotsFeatured case study · Sept 2025
Teva went looking for a way out of multi-week content review cycles on Veeva CRM. We designed an agentic AI layer that drafts, pre-checks, and routes compliant content before a human reviewer ever opens it — starting with a contained pilot in one business unit.
Pilot Impact — Projected Outcomes · Teva Pharmaceuticals
MLR approvals in weeks, not months
Workflow accelerationContent live before market windows close
Speed to marketProjected per business unit, pilot alone
Operational efficiencyPer week saved on a high-value launch
Business growthThe challenge
Teva's Veeva CRM workflows weren't broken. They were built for a different era — one where a marketing email could afford to sit in review queues for weeks while competitors moved.
Every HCP email, CLM slide, and detailing aid moved through medical, legal, and regulatory (MLR) review by hand. Reviewers read full documents line by line. Teams worked in sequence, not in parallel. And in the generics business, weeks lost in review aren't an inconvenience — they're erosion risk, with every delayed launch handing margin to whoever ships first.
Manual re-submissions added audit-finding risk on top. The cost wasn't just time; it was missed windows, regulator exposure, and a content ops budget that grew with headcount instead of output.
What we designed
We didn't propose replacing Veeva, and we didn't propose replacing reviewers. We designed AI agents to handle the repetitive 80% — drafting, checking, tagging, logging — so humans only touch decisions that need judgment.
Generative AI drafts HCP emails, detailing aids, and meeting summaries from pre-approved brand libraries — contextualized per therapeutic area, HCP segment, and region, with version control aligned to global brand guidelines.
AI compliance guardrails flag prohibited terms, off-label claims, and missing references before submission. Reviewers see auto-tagged risk areas instead of full documents, and parallel workflows let medical, legal, and regulatory work simultaneously.
Targeted business-unit training and UAT workshops built into the rollout — because automation nobody uses is just expensive shelfware. Industry data shows 80%+ adoption within 6 months when this is done right.
A brand or field team raises a content need.
Workflow RoutingAgents generate copy from approved brand libraries.
Content GenerationAuto-validation against FDA, EMA, and GDPR rules.
Risk DetectionMLR teams review pre-tagged risk areas in parallel.
Human OversightApproved content goes live, with a full audit trail.
Enterprise DeploymentROI & competitive benchmarks
Where Teva stands today, what the pilot changes, what the rest of the industry has already published — and what each line is worth.
| Category | Current state | With automation (pilot) | Industry benchmark | ROI for Teva |
|---|---|---|---|---|
| MLR approval cycle | Slow, sequential approvals | 40–50% cycle reduction — avg. 3–4 week cycles | Pfizer: 35% faster approval cycles | ~$200K annual savings per business unit |
| Speed to market | Weeks lost in review delays; generics face erosion risk | Launches 2–3 weeks faster, content live sooner | Novartis: 50% faster content availability · Sanofi: 20% higher HCP engagement | Each week saved on a high-value launch = $1M+ potential revenue |
| Compliance risk | Manual re-submissions; risk of audit findings | AI compliance guardrails + automated audit trails | Eli Lilly: 70% drop in resubmissions after automation | Lower risk of fines, rework, and reputational damage |
| Change management | Manual processes; adoption challenges | Targeted BU training + UAT workshops = smooth adoption | Industry avg.: 80%+ adoption within 6 months | ROI actually realized — no shelfware effect |
| Competitive positioning | Lagging behind automation leaders | AI-enabled workflows live in pilot BU | Pfizer, Novartis & Sanofi have already scaled automation | Parity with leaders at a leaner cost base |
Pilot-stage impact model prepared for Teva Pharmaceuticals, Sept 2025. Industry figures from published implementations.
How we deliver — every engagement
Enterprise AI fails when it launches everywhere at once. Our delivery model starts small on purpose — one business unit, a sandbox, clear KPIs — and earns each expansion. It's the same playbook whether you're in pharma, finance, or logistics.
Workshops with every stakeholder group — for Teva, that meant medical, legal, marketing, and IT — to define what "fast and compliant" actually means.
AI workflows run in a sandbox against real content and real processes. Contained risk, honest results.
Pre-checks, parallel review, and audit automation go live with the teams who feel the bottleneck most.
The pilot's playbook scales across business units, regions, or therapeutic areas — tuned per context, not copy-pasted.
Full deployment with Power BI / Tableau dashboards tracking cycle time, error rates, adoption, and ROI per asset.
Common questions
Six things: agentic AI workflow automation, generative AI content systems, AI compliance guardrails, custom AI assistants and chatbots, predictive analytics, and enterprise integration with platforms like Veeva, Salesforce, SAP, and Oracle. Most engagements combine two or three.
A chatbot answers questions. Agentic AI completes work — it drafts the document, checks it against your rules, routes the approval, and logs the audit trail, across multiple steps, without waiting for a prompt at each one. Your people stay in the loop for decisions, not for repetition.
Yes — the leaders already publish their numbers. Pfizer cut initial content development time by 55% with AI-assisted creation. Eli Lilly saw a 70% drop in resubmissions after automation. Novartis reports 50% faster content availability. The trick is building compliance into the pipeline, not bolting it on after.
Pilots run in a single business unit over roughly one quarter. In the impact model we built for Teva, a single-BU pilot projects around $200K in annual savings — and on a high-value product launch, every week saved is worth $1M+ in potential revenue. We agree on KPIs before anything is built, so ROI is measured, not claimed.
Change management is part of the build, not an afterthought. Targeted business-unit training, UAT workshops with the actual end users, and adoption tracked as a KPI alongside cycle time. Industry data shows 80%+ adoption within 6 months when rollouts are done this way — and near-zero when they aren't.
Veeva CRM and Vault, Salesforce, SAP, Oracle, SharePoint, Box, Okta, Azure AD, Power BI, and Tableau — plus custom internal systems via API. The goal is one ecosystem where content, identity, pricing, and analytics never wait on a manual handoff.
Tell us where your workflows slow down. We'll map what an agentic AI pilot would look like in your environment — scope, timeline, and projected ROI — before you commit to anything.
