# Synq AI - Full Content Reference > This file contains comprehensive, machine-readable content from every live page of www.synqflow.ai. > Concise summary: https://www.synqflow.ai/llms.txt > Sitemap: https://www.synqflow.ai/sitemap.xml > Canonical host: https://www.synqflow.ai (the bare synqflow.ai redirects to it) > Cite as: Synq AI, https://www.synqflow.ai --- ## COMPANY Synq AI is an enterprise AI infrastructure company, incubated by ACIC VGU Foundation and CU-TBI (Chandigarh University Technology Business Incubator). Synq AI builds the AI context layer beneath enterprise AI. It grounds any model in an organisation's institutional knowledge, with provenance from source to answer, so agents act with the judgment of the organisation's best people instead of generic knowledge. Company: Synq AI Email: swastik@synqflow.ai Phone: +91 92204 07939 Website: https://www.synqflow.ai ### Team - Pratik Sud, Co-Founder and CEO - Swastik, Co-Founder and Chief AI Officer --- ## HOME (https://www.synqflow.ai) ### What the page says The headline is "Bringing AI to the Real Economy". Synq AI builds AI systems that help businesses access the right information, resolve operational bottlenecks and automate everyday work. The first section names the problem: "Most businesses have AI, and their work still runs the old way." Information is everywhere but answers are not, workflows hold businesses back, and turning AI into reliable business systems is the hard part. Synq AI grounds enterprise AI in an organisation's institutional context, so every model acts with the judgment of the organisation's best people, not generic knowledge. The recurring thesis across the page: "Context is the difference between AI that demos and AI that ships." Synq grounds AI in institutional knowledge so it acts with the judgment of an enterprise's best people, not the ignorance of a new hire. ### Why Synq Three value points repeated on the home page: - Real-time context: the AI stays current because Synq ingests documents, calls, and decisions continuously, so no query starts from zero. - Institutional memory: decisions, processes, and know-how stop being locked in one person's head or inbox. - Human in the loop: the system recognises the limits of its own certainty and keeps people in control at the edges. ### Industries referenced The home page and enterprise hero repeat the same set of target industries: Defence, Infrastructure, AI Research Labs, Finance, Healthcare, Energy, Logistics, and Public Sector. These are industry categories the site targets, not named clients in every one of them (see PORTFOLIO below for the three named, live clients). --- ## ENTERPRISE (https://www.synqflow.ai/enterprise) ### What the page says Reliable AI for the decisions an enterprise runs on. Synq is the AI context layer beneath enterprise AI, grounding every model in an organisation's institutional knowledge so it acts with the judgment of its best people. Synq connects every model to the enterprise knowledge graph, so agentic AI reasons with institutional context instead of generic training data. ### Three pillars 1. Context Layer - the AI context layer that grounds every model in institutional knowledge, with provenance from source to answer. 2. Enterprise Knowledge Graph - an organisation's knowledge structured into a living graph of entities, relationships, and decisions. 3. Agentic Execution - agentic AI that acts on grounded context, executing multi-step work with judgment teams can audit and trust. ### Trusted by, live in production Three real clients have production systems and quotes on the live site (the same three also appear on the PORTFOLIO page). They are described by sector rather than named: **A facilities and operations company** - Founder (also a co-founder of Synq AI): "Synq automated the operational backbone of our business: complaint raising, query resolution, POC management, lease-expiry tracking, legal document preparation, and centre-wise finance MIS. Work that used to be manual now runs itself, grounded in how we actually operate." **An electric mobility operator** - Head of Data: "When a battery's IoT stream flags a fault into our S3 bucket, Synq's agent picks up the raw data, refines it, and hands clean context straight to our on-ground team to act on. Data triage that used to be manual is now autonomous." **A safety equipment manufacturer** - Head of Commercial Excellence: "Synq automated our deal pricing end to end. SKU margins now calculate themselves and route through L1, L2, and L3 approvals over email automatically. Pricing decisions that took days now close in minutes." ### Platform overview on this page The enterprise page shows the Synq platform workspace covering: Sources, Knowledge Graph, Retrieval, Grounding, Agents, Evaluations, Governance, and Audit. Two stated properties: the platform is model agnostic (any model can be grounded in the same context, with no lock-in to one provider), and every retrieval and agent action is permissioned, logged, and traceable end to end. ### Security and trust on this page The enterprise page states: encryption, access controls, and audit trails are built into every layer; agents operate within clear guardrails with every action logged and reviewable; models can be swapped freely without losing the context layer or governance controls. (Note: illustrative sample metrics and sample compliance seals also appear in this section of the live page for visual placeholder purposes only; they are not confirmed customer outcomes or held certifications and are intentionally omitted from this file.) --- ## PORTFOLIO (https://www.synqflow.ai/portfolio) ### What the page says Proof of work. Live production systems, running inside real enterprises. Twelve named systems are live today across three enterprises. ### A facilities and operations company - Operations (6 systems, all live) 1. Complaint Management System - automates complaint raising, routing, and resolution tracking end to end. 2. POC Accountability Tracker - assigns and tracks a point of contact for every item of work, with owners, status, and deadlines visible in one place. 3. Lease Management System - tracks lease expiries across the portfolio and raises renewal alerts ahead of time for the business development function. 4. Finance MIS Engine - generates the monthly centre-wise finance MIS reports automatically from Tally, replacing a manual monthly assembly. 5. Visitor Management System - digitises visitor check-in, records, and host notification. 6. Meeting Room Management System - handles meeting-room booking, conflicts, and utilisation across the office. ### A safety equipment manufacturer - Commercial (3 systems, all live) 1. Deal Flow Management System - runs the deal pipeline end to end, from quote construction through to a closed, approved price. 2. Margin Calculation Engine - calculates margins automatically at SKU level across the deal, removing the manual spreadsheet pass. 3. Multi-Level Approval Workflow - routes pricing approvals automatically through L1, L2, and L3 over email, with the full approval trail retained. ### An electric mobility operator - Data and IoT (3 systems, all live) 1. Battery Fault Ingestion Pipeline - picks up battery IoT fault data the moment it lands in the storage bucket, with no manual polling. 2. Data Refinement Agent - an agent takes the raw telemetry, refines it, and resolves it into a clean, actionable fault record. 3. Field Dispatch Context Layer - passes the refined context straight to the on-ground team so the right person can act on the right fault immediately. No outcome metrics (time saved, percentage improvements, revenue figures) are published for these systems. The page states real scope only, no invented figures. --- ## RESEARCH (https://www.synqflow.ai/research) ### What the page says AI fails on context, not intelligence. Synq AI's research is on one question: can an answer about your business be trusted? Grounded in your own records, run on your premises, and judged against questions your team writes. ### The problems Synq AI works on 1. Grounding an answer 1.1 Keeping answers current (in use) - sources are read again on a schedule, and every answer rests on what was read last. 1.2 Citing the exact source (in use) - every answer names the document and the line it stands on. 1.3 Reading only what the asker may see (in use) - an access guard sits in front of every search, so withheld records never reach the model. 2. Judging an answer 2.1 Knowing when it does not know (in use) - when it is not sure, it says so and shows what it found. 2.2 Grading grounding, not fluency (studying) - every answer is graded by a person against the sources it cited; Synq AI is studying how to grade faster without losing that check. 3. Acting on an answer 3.1 Agent jobs that stop for a person (in use) - a job proposes, a person approves, and every step is kept on the record. ### How Synq AI evaluates 1. Write the questions together - before anything goes live, the client's team writes the questions it needs answered and names the records that hold each answer. 2. Grade every answer - each answer gets a verdict, question by question, with the sources it cited in front of the person grading it. 3. Say when it is not sure - an answer without enough support says so and shows what it found, instead of guessing. 4. Feed the ratings back - rated answers go back into search. 5. Keep every miss - a miss joins a standing regression gate, is asked again on every run, and a change that breaks it does not ship. ### Anatomy of a miss (illustrative) An illustrative example in a distributor's records. Asked what interest applies to late payments under a 2024 distribution agreement, the system found clause 11.4, which points to Schedule 3, but could not see Schedule 3, so it said it was not sure and showed what it found. The grader marked it a miss: the schedule had been scanned as a separate file and never joined to the agreement. The fix joins separately scanned schedules to their agreement when they are read, and the question joined the regression gate. On the next run the answer cited both records and passed. --- ## BLOG (https://www.synqflow.ai/blog) Notes on reliable AI. Working notes on grounding, provenance, and the context layer enterprise AI runs on. Two posts are published: ### What is the AI context layer? URL: https://www.synqflow.ai/blog/what-is-the-ai-context-layer A field guide arguing that new models keep arriving smarter while enterprise AI pilots still stall before production, because the missing ingredient is context, not intelligence. It defines an AI context layer as the tier between enterprise data and AI agents that turns raw information into governed, actionable business meaning, and lays out six stages a context layer is made of: Capture, Structure, Graph, Retrieve, Ground, Serve, with governance and provenance underneath all of them. It distinguishes a context layer from a semantic layer (built for BI and dashboards) and from plain retrieval-augmented generation (similarity search with no governance or entity awareness). The post cites third-party research (attributed to MIT in its sources) for a claim that a large majority of enterprise AI agent pilots never reach production, and cites other third-party sources on institutional knowledge loss and average knowledge-worker tenure. These figures are the post's own cited claims, not Synq's independently verified data. ### Why every enterprise AI pilot fails URL: https://www.synqflow.ai/blog/why-enterprise-ai-pilots-fail A perspective piece arguing that pilots which succeed in testing routinely fail to scale because the AI lacks tacit knowledge of how the enterprise actually works: implicit knowledge held by people that is absent from CRM, HRMS, and ERP systems. It argues pilots appear to work only because humans manually compensate for this context gap, and that removing that compensation at scale causes output quality to collapse. It proposes that what is missing is a persistent, queryable context layer representing how the enterprise works, not another data warehouse or integration, and closes with advice for AI buyers to ask any vendor how their system actually learns how the buyer's company works. --- ## ABOUT (https://www.synqflow.ai/about) ### What the page says About us. Synq AI builds the context layer for enterprise AI: a small team in Gurugram, India, incubated by ACIC VGU Foundation and CU-TBI. The page carries a letter from the founders, the commitments the company holds itself to (data stays in the client's building, every answer shows its source, it must pass the client's own questions before go-live, no client named without written permission, no number published that cannot be defended, and the team runs its own company on it), where the team works, and how to reach a person. ### Contact details shown on the page - Email: swastik@synqflow.ai - Phone: +91 92204 07939 - LinkedIn: linkedin.com/company/synqflowai - Book a working session: https://www.synqflow.ai/book-demo ### Team - Pratik Sud, Co-Founder and CEO - Swastik, Co-Founder and Chief AI Officer ### Assessment The page offers a short self-assessment: answer a handful of questions and get an indicative read on where an organisation's AI stands today. The page is explicit that this is a starting point, not a certified audit. --- ## ACADEMY (https://www.synqflow.ai/academy) ### What the page says Team training for companies: a company sends the people who will own, build and check its AI. The roadmap runs eighteen weeks, in two halves. The first half, make it know, walks one invoice through the six stages a document passes through in the system. The second half, make it act, covers finding the work worth automating, running one automation against a second invoice that does not match, and keeping a person in the loop with a run log of what ran alone and what waited. ### Tracks Three seats a company sends: Operators, Builders, Evaluators. ### The capstone The course ends the way a deployment does: the team's system is graded against questions the team wrote itself, and against an automation run. ### What the page does not say No dates, no price and no teacher names. The cohort is not open; the page takes an expression of interest. --- ## BOOK A DEMO (https://www.synqflow.ai/book-demo) ### What the page says Thirty minutes with the live system, on the visitor's own work. The form asks what work they want AI to take on, the systems it lives in, and where they are today; the answer that comes back is a plan for a first system, not a brochure. ### What happens to what is typed The form writes one row to a private sheet the team reads. It is checked for bots at the moment of submitting. See the privacy policy for what is kept and for how long. --- ## LEGAL (https://www.synqflow.ai/privacy, /terms, /cookies) ### What the pages say The privacy policy describes this website only: what each form asks for, that every voice conversation is saved word for word, that conversation rows are deleted after 90 days while form rows are kept, that the visitor's IP address is read only to rate limit, and that the site sets no cookies. What happens to a customer's data inside a deployment is set by the signed agreement, not by these pages. The terms cover use of the website. The cookie policy states plainly that no cookies are set. --- ## FULL PAGE LIST - https://www.synqflow.ai - Home - https://www.synqflow.ai/enterprise - Enterprise: what you get, governance, cost, what you sign - https://www.synqflow.ai/products/saffron - Saffron: a private brain for your company, on your premises - https://www.synqflow.ai/portfolio - Portfolio: every product, and twelve live production systems - https://www.synqflow.ai/research - Research: the AI context layer as an open problem - https://www.synqflow.ai/blog - Blog index - https://www.synqflow.ai/blog/what-is-the-ai-context-layer - Blog post - https://www.synqflow.ai/blog/why-enterprise-ai-pilots-fail - Blog post - https://www.synqflow.ai/about - About us: the founders, what we hold ourselves to, where we are, contact - https://www.synqflow.ai/academy - Academy: team training, the eighteen-week roadmap, three tracks - https://www.synqflow.ai/book-demo - Book a demo: thirty minutes with the live system - https://www.synqflow.ai/privacy - Privacy policy: what this website collects and keeps - https://www.synqflow.ai/terms - Terms of use - https://www.synqflow.ai/cookies - Cookie policy: this site sets no cookies - https://www.synqflow.ai/sitemap.xml - Sitemap - https://www.synqflow.ai/llms.txt - Concise LLM summary - https://www.synqflow.ai/llms-full.txt - This file