Nsoffice
Written by the Bee, read it before you send it
KPIMore first meetings turn into real deals, on less preparation time
I am meeting her for the first time, I want to pitch a partnership
Network Science hired Shefta Rizwan into a dedicated Strategic Growth Manager (AI & Deep Tech) role in July 2025 \u2014 a direct signal that they are actively building out their partner ecosystem right now; this is the exact window to get Glean designed into the AI Office delivery model before the partner roster is locked.
Glean Search
Glean Search high
Sourcing and formalising technology partnerships that strengthen Network Science's 70+ AI/Deep Tech ecosystem — Glean would be a direct addition to that roster.
Shefta Rizwan
Strategic Growth Manager (AI & Deep Tech) at Network Science / NSOffice.AI · ~1 year (joined July 2025 in current role; prior years in inside sales and campaign generation at Network Science)
Shefta holds an Executive MBA in Business Management from NMIMS CDOE (2021–2023) and has spent her career at Network Science, moving from Inside Sales Specialist — running cold-call, email, LinkedIn, and tender-based outbound campaigns to C-suite buyers (CEOs, CTOs, CHROs, CDOs, CLOs) — to her current role as Strategic Growth Manager (AI & Deep Tech). She is specifically tasked with expanding Network Science's strategic partnerships and positioning the firm as a leader in enterprise AI transformation. Her LinkedIn about section explicitly frames her mandate as "driving growth and expanding our strategic partnerships." With 4,352 connections and a GTM-strategist headline, she is the right door into the Network Science partner ecosystem.
- Sourcing and formalising technology partnerships that strengthen Network Science's 70+ AI/Deep Tech ecosystem — Glean would be a direct addition to that roster.
- Building a compelling, differentiated partner stack for TurinOS so NSOffice.AI clients get best-in-class tooling at every layer of the enterprise AI stack.
- Generating pipeline through co-sell and co-marketing motions with Glean — joint case studies, referral agreements, or reseller arrangements that let her hit growth targets.
- Positioning Network Science as a credible 'AI Operating System' by associating with recognisable enterprise AI brands — Glean's brand strengthens that story in CIO conversations.
- Demonstrating measurable ROI (they publicly claim 8X) to enterprise clients, which requires verified, production-grade tooling — not point solutions — across search, knowledge, and workflow automation.
- Enterprise AI strategy and transformation — her current role description frames Network Science as 'the operating system for the enterprise AI world'
- GTM strategy and sales motions targeting senior executives (CEO, CTO, CHRO, CDO) — explicitly called out in her LinkedIn about section
- Deep Tech and AI startup ecosystem curation — Network Science works with 70+ AI/Deep Tech startups globally, and Shefta manages partner growth in that ecosystem
- Knowledge management and AI use-case scaling — TurinOS is described as housing 400+ curated use cases tied to business KPIs
- Professional development and continuous learning — she notes staying current on industry trends and best practices as a personal commitment
Nsoffice today
- Industry
- Enterprise AI Consulting & AI Platform (AI-as-a-Service / AI Operating System)
- Size
- Small-to-mid-stage startup / boutique consultancy; exact headcount not publicly disclosed. LinkedIn follower count and the scale of claims (15+ AI Offices, 100+ clients) suggest a team likely in the 50–200 range. Not publicly funded on Crunchbase or listed on stock exchanges.
- HQ
- Mumbai, Maharashtra, India
- Footprint
- Headquartered in Mumbai, India — primary market is Indian enterprise, with global client claims suggesting outreach to APAC, Middle East, and potentially Europe. · Operates across industries including manufacturing, BFSI, healthcare, retail, and professional services — inferred from the 400+ use cases and multi-industry AI Office deployments described in TurinOS positioning. · Maintains a curated ecosystem of 70+ AI/Deep Tech startup partners globally — effectively a marketplace or integrator role in addition to consulting. · Website domain nsoffice.ai suggests a deliberate pivot toward an AI platform identity, distinct from a pure consulting brand.
Network Science is an enterprise AI transformation firm that brands itself as "the Operating System for the Enterprise AI World." It delivers two core offerings: (1) AI Office as a Service — embedding dedicated AI teams inside client organisations to co-own strategy, design, and full-scale execution of AI programs; and (2) TurinOS — a proprietary AI Operating System that acts as a central control tower connecting client vision to execution through 400+ curated AI use cases, real-time market signals, and business-aligned KPIs. The firm claims 150+ AI projects delivered, 100+ global enterprise clients, 15+ AI Offices deployed, and an 8X ROI benchmark. It also curates and resells/integrates a portfolio of 70+ AI and Deep Tech startups worldwide.
- Launched NSOffice.AI as a distinct brand/product identity (nsoffice.ai domain active as of 2024–25), signalling a platform-led go-to-market pivot away from pure consulting.
- Shefta Rizwan promoted/hired into a newly designated Strategic Growth Manager (AI & Deep Tech) role in July 2025 — direct signal of accelerated partnership investment.
- Publicly claims 15+ AI Offices deployed as of 2025 position description — indicating active scaling of the embedded AI team model.
- Actively expanding the 70+ AI/Deep Tech startup partner ecosystem, positioning TurinOS as the integration layer across all these tools — suggesting ongoing partner onboarding activity.
- TurinOS described as housing 400+ curated use cases with real-time market signals and KPI alignment — a product expansion that requires best-in-class knowledge and search infrastructure to deliver.
- Enterprise clients suffer from fragmented AI pilots that never scale — Network Science's own positioning ('AI from scattered pilots and disconnected experiments') names this as the core problem they solve, implying their clients arrive with siloed, ungoverned AI sprawl.
- Knowledge fragmentation inside client organisations undermines the ROI of AI deployments — if employees can't find information across the SaaS stack, AI agents built on TurinOS can't retrieve accurate context, degrading answer quality and the 8X ROI claim.
- Vendor selection risk for clients: with 70+ startup partners in their ecosystem, Network Science must vet and validate which tools actually deliver — a Glean partnership gives them a proven, Gartner-recognised enterprise search layer to anchor recommendations.
- Speed-to-value pressure: the AI Office as a Service model requires Network Science to show measurable ROI quickly inside client orgs — without a unified knowledge retrieval layer, AI agents hallucinate or surface stale data, slowing deployment timelines.
- Search and knowledge retrieval gap in TurinOS: TurinOS is described as a 'control tower' for AI use cases, but there is no public mention of an enterprise search or RAG layer within it — this is a capability gap Glean can fill directly.
- Competitive differentiation pressure: as more India-based AI consultancies emerge (e.g., Fractal, LatentView, Persistent Systems), Network Science needs tier-1 tech partnerships to win enterprise mandates against larger incumbents.
- Platform-first identity via NSOffice.AI — the new domain and TurinOS branding signal a deliberate shift from consulting to a scalable, product-led model that can be sold and deployed repeatedly.
- AI Office as a Service at scale — targeting 15+ AI Office deployments as a repeatable franchise model inside large enterprises, requiring robust tooling to standardise delivery.
- Ecosystem monetisation — the 70+ startup partner network is a strategic moat; Network Science aims to be the trusted curator/integrator for enterprise AI procurement, not just a builder.
- ROI accountability as differentiator — the public '8X ROI' claim is a bold proof-point bet; they are staking commercial credibility on measurable outcomes, which requires reliable underlying technology.
- Global enterprise expansion — describing clients as '100+ global enterprises' and framing themselves as a worldwide AI operating system signals ambition beyond India into APAC, MENA, and Western markets.
- Use-case industrialisation via TurinOS's 400+ use case library — this is a scalability bet: rather than bespoke engagements, they aim to match enterprise problems to pre-validated AI patterns rapidly.
Positioning
Network Science has made a bold bet: that enterprises need an AI Operating System \u2014 TurinOS \u2014 to move from scattered pilots to business-critical AI at scale. Glean is the connective tissue that makes that bet pay off \u2014 a permissions-aware enterprise knowledge layer with 100+ native connectors that gives TurinOS and every AI Office deployment accurate, real-time context from the client's actual SaaS stack, eliminating the hallucination and knowledge fragmentation that kill AI ROI before it's ever measured. Together, Glean as the retrieval and knowledge layer plus TurinOS as the orchestration and use-case layer is a more complete enterprise AI stack than either company offers alone.
Pitches
Glean Search
Network Science's own positioning names 'scattered pilots and disconnected experiments' as the core disease in enterprise AI — and fragmented knowledge is the primary cause. Glean Search gives every AI Office deployment a single, permissions-safe index across 100+ enterprise apps from day one, so TurinOS agents and embedded AI teams are retrieving from a unified, live knowledge base rather than fighting tool-by-tool data access. That's the difference between an AI Office that shows 8X ROI in month three versus one that's still doing data plumbing in month six.
Glean Assistant
TurinOS orchestrates use cases and KPIs — but employees inside client orgs need a trusted interface to ask questions and get cited, accurate answers without prompting an AI hallucination. Glean Assistant is a conversational work assistant grounded in the company's own indexed knowledge, with citations back to source documents, deployed without any fine-tuning or RAG pipeline work from your team. Bundled into an AI Office engagement, it gives clients a visible, day-one win that employees actually use — which is the adoption proof point Network Science needs to defend its ROI commitments.
Glean Apps
Network Science is industrialising AI deployment via 400+ pre-validated use cases — Glean Apps accelerates that model by giving Network Science's delivery team a no-code/low-code platform to build HR bots, IT assistants, and sales enablement agents directly on top of the client's Glean-indexed knowledge graph, without building RAG pipelines from scratch for each client. Rather than each AI Office being a bespoke build, Glean Apps becomes the repeatable agent-builder layer that makes the franchise model actually scalable.
Conversation arc
“Shefta, I've been following what Network Science is building with TurinOS — the 'AI Operating System' framing is exactly the right narrative for where enterprise buyers are right now. I wanted to talk because I think there's a layer of the stack where we can make each other's story stronger, not compete. Does that feel like the right way to use our time today?”
“When your team embeds inside a client's organisation and starts standing up an AI Office, what does the knowledge infrastructure look like on day one? Are employees typically able to get accurate answers from AI agents you deploy, or is there usually a messy discovery phase where the underlying knowledge is scattered across Confluence, SharePoint, Slack, and five other tools?”
“Here's what we see consistently: AI agents that are built without a unified knowledge layer start hallucinating or surfacing stale data within weeks, and that erodes the ROI story fast. Glean indexes 100+ enterprise apps — Slack, Confluence, Salesforce, GitHub, SharePoint — respects every permission so employees only see what they're authorised to see, and serves that indexed knowledge to whatever AI layer sits on top, including TurinOS. For your AI Office teams, that means you walk into a client on day one with a retrieval layer that's already trusted, already connected, and already accurate — which protects the 8X ROI claim you're making to the market.”
“That makes sense — I'd expect TurinOS to have some retrieval capability built in. The question is whether it's maintaining live, permissions-aware indexes across 100+ enterprise SaaS apps simultaneously across every client's unique stack, or whether it's doing retrieval against a subset of structured data you've pre-ingested. Most platforms do the latter, and that's where the knowledge gaps show up in production. We'd love to understand how TurinOS handles that specifically — because if there's a gap, Glean can fill it without displacing anything you've built.”
“Here's what I'd suggest as a first step: let us run a 30-minute technical deep-dive with whoever on your team is responsible for AI Office delivery architecture — show them exactly how Glean's connector framework and permissions model would work inside a live client environment. If it fits, we can explore a formal partner agreement with co-sell and co-marketing. Would that be worth setting up in the next two weeks?”
Ask these
- Q1When your AI Office team first embeds inside an enterprise client, how do you assess the state of the client's knowledge infrastructure? Specifically, do you find that AI agents you deploy on TurinOS are retrieving accurate, up-to-date information from across the client's full SaaS stack — or is there usually a gap between what the AI can access and what employees actually know and use?
- Q2TurinOS lists 400+ curated AI use cases tied to business KPIs — which of those use cases are most commonly deployed first in an AI Office engagement, and in those deployments, what tool is handling the knowledge retrieval layer underneath the AI response? Is that something TurinOS handles natively, or is it something your delivery teams are solving client-by-client?
- Q3You work with 70+ AI and Deep Tech startup partners globally — how does a new partner typically get evaluated and brought into that ecosystem? Is there a formal integration requirement with TurinOS, a co-sell motion, a revenue-share model, or some combination? I want to understand what a successful partnership looks like from your side before we talk about what we'd propose.
- Q4The 8X ROI claim is bold and specific — I'd love to understand how you measure and defend that in enterprise sales conversations. What's the primary driver of that ROI — is it employee time saved, faster decision-making, reduced headcount requirements, or something else? The reason I ask is that Glean's own customer data shows a measurable reduction in time-to-answer and onboarding time, and I'm curious whether those metrics map to how Network Science reports outcomes to clients.
- Q5As you scale from 15 AI Offices to more, what's the biggest delivery risk you're managing? Is it talent — finding enough people to embed in each org — or is it technology standardisation, where each AI Office deployment ends up custom-built and hard to replicate? Understanding where the friction is helps me think about where Glean could reduce it.
Feasibility
FAQs
'We already handle knowledge retrieval inside TurinOS — we don’t need a separate search layer.'
That's worth pressure-testing together. TurinOS is built as a use-case orchestration and KPI control tower — it's genuinely excellent at that. But enterprise knowledge retrieval at the level Glean operates — live, permissions-aware indexing across 100+ SaaS apps simultaneously, with ACL enforcement from each source system — is a dedicated infrastructure problem that takes years to build natively. The question isn't whether TurinOS can retrieve information; it's whether it's maintaining a live, trusted, permission-safe index across every app inside every client's heterogeneous environment. If it is, we should talk about where Glean's connector library adds coverage. If it isn't, Glean closes that gap without displacing anything TurinOS already does.
'Our clients are mostly in India — Glean isn’t well-known here and may not resonate with Indian enterprise buyers.'
Glean is deployed at some of the world's largest enterprises — including companies with major India operations — and is increasingly active in the APAC and India market. More importantly, the value proposition in Indian enterprises is identical to global ones: knowledge workers spend hours per week searching across fragmented SaaS stacks, and AI agents hallucinate without a trusted knowledge retrieval layer. The ROI math works in Mumbai the same way it works in New York. And for Network Science specifically, adding a Glean partnership actually strengthens your positioning in international client conversations — you're able to say your AI Office deployments are powered by a globally recognised enterprise search standard.
'We have a full ecosystem of 70+ startup partners already — adding Glean is just more noise in an already crowded partner stack.'
Understood — and that's exactly the right instinct to push back on. But there's a meaningful difference between a point-solution AI startup in the ecosystem and a foundational infrastructure layer. Glean isn't competing with the LLM fine-tuning startup or the document processing tool in your partner roster — it's the retrieval and knowledge layer that makes all of them more accurate. Think of it as the plumbing that the rest of your ecosystem runs on top of. The 70+ partners generate more value, not less, when they're drawing from a unified, trusted knowledge index. That's a different conversation than adding the 71st point solution.
What they appear to run
- TurinOS described as '400+ curated use cases with real-time market signals' — implies a data ingestion and indexing layer that could benefit from or compete with enterprise search.
- Network Science positions itself as an integrator of 70+ AI/Deep Tech startups — signals a multi-vendor, heterogeneous tech environment where a unified knowledge layer (Glean) would reduce friction.
- Shefta's LinkedIn profile confirms active use of LinkedIn Sales Navigator and email marketing platforms internally — these are classic SaaS stack components Glean can index.
- No public mention of an enterprise search, intranet, or knowledge management vendor in any Network Science-owned content — a clear gap in the stated tech estate.
- AI Office as a Service deployments inside client orgs implies Network Science's teams operate within client SaaS stacks (Slack, Confluence, SharePoint, etc.) — precisely the fragmented environments Glean is built to unify.
- Company's own pain point narrative ('AI from scattered pilots and disconnected experiments') signals awareness of knowledge fragmentation — they counsel clients on this problem, meaning they understand and can articulate Glean's value proposition.