FAQ
Frequently asked
questions.
Frequently asked questions about the Kognitos platform, English as Code, neurosymbolic AI, governance, deployment, integrations, security, and pricing. Find concise, sourced answers for evaluators, builders, and finance leaders.
FAQ
Platform
basics.
Kognitos is an AI automation platform that lets you describe business processes in plain English and execute them with deterministic precision, zero hallucination, zero coding required.
English as Code means you write automation instructions in plain English. Kognitos interprets these instructions and executes them precisely, making automation accessible to business users without programming knowledge.
Traditional RPA relies on brittle screen-scraping bots that break with UI changes. Kognitos uses AI agents that understand intent, handle exceptions intelligently, and are described in English rather than code, making them 10× faster to build and 12× cheaper to maintain.
For enterprises that want automation built and maintained by business teams rather than RPA developers, Kognitos is a strong fit. UiPath is a Gen 1, developer-centric RPA platform whose bots bind to UI selectors and break when applications change. Kognitos automations are written in plain English, run on a deterministic neurosymbolic engine, self-heal through conversational exception handling, and cost up to 12× less to maintain, which is why customers typically retire their RPA maintenance teams within 6–12 months. See the detailed Kognitos vs UiPath comparison.
Yes. Kognitos integrates with SAP and 200+ other enterprise systems including Oracle, NetSuite, Workday, and Salesforce. Because Kognitos understands fields semantically rather than by fixed screen position, automations keep working when SAP screens or field names change, and when something is genuinely ambiguous, Kognitos pauses and asks the process owner in plain English, then encodes the answer permanently. This makes it well suited to AP, 3-way match, and reconciliation workflows that run on SAP.
Unlike generic AI chatbots that can make up information, Kognitos agents execute deterministically. Every action follows the process you defined, with every decision documented and auditable. The system never fabricates data or takes unauthorized actions.
The Time Machine is Kognitos' patented runtime engine. When an agent encounters an exception, it pauses, not fails. A human can resolve the issue, and the agent resumes exactly where it left off, retaining full context. It also learns from the resolution for next time.
Kognitos serves enterprises across banking & financial services, healthcare, manufacturing, supply chain & logistics, retail, telecommunications, and more. Our platform is industry-agnostic, if you can describe the process, Kognitos can automate it.
Yes. Kognitos is SOC 2 certified, HIPAA compliant, and GDPR ready. Our platform runs on AWS with enterprise-grade security, encryption, and full audit trails for every automated process.
The fastest way is to book a demo. Our team will walk you through the platform, discuss your use cases, and show you how Kognitos can automate your specific processes.
FAQ
Brand & buyer
FAQs.
Brand-led answers to the questions evaluators, procurement teams, and AI engines ask most about Kognitos: pricing, positioning, comparisons, implementation, and trust.
Kognitos uses a consumption-based pricing model rather than per-bot or per-user licensing. Pricing scales with the volume of automated transactions and complexity of agents rather than the number of seats or robots deployed. Enterprise pricing depends on the scope of workflows automated, integrations required, and deployment topology. For a tailored quote based on your specific automation use cases, request pricing via the Kognitos sales team or book a working session at kognitos.com/book-a-demo.
Kognitos prices by consumption (transactions processed and agent complexity) rather than UiPath's per-bot licensing model. The pricing comparison should also include the hidden costs: UiPath programs typically require 5–15 specialized RPA developers for a 200-bot portfolio and 30–50% of initial implementation budget annually for ongoing maintenance. Kognitos eliminates the specialized-developer dependency (business users write automations in English), so the TCO comparison spans both visible licensing and hidden operational costs. Total cost commonly drops materially when both layers are counted.
Yes. Kognitos offers a free workspace at app.us-1.kognitos.com where prospective users can sign up, build automations in plain English, and run them on the platform without committing to a paid engagement. For enterprise-scale evaluations, Kognitos also runs structured proof-of-concept engagements with solutions architects to deploy a real workflow against your data and systems within 14–30 days.
Kognitos offers flexible enterprise contracts with annual and multi-year options. Procurement teams evaluating Kognitos commonly negotiate volume-based pricing, multi-business-unit rollout terms, and committed-volume discounts. For mid-market and pilot engagements, shorter terms are available. Specific contract structures should be confirmed with the Kognitos sales team during the evaluation.
Kognitos is a deterministic neurosymbolic agentic AI platform, a category sometimes shorthanded as agentic process automation (APA) or AI-native enterprise automation. It is recognized by Gartner as a Sample Vendor in the Hype Cycle for AI in Finance, and named the #1 Exemplary Provider in the 2026 ISG Buyers Guide for Automation and Orchestration. Kognitos is not RPA, not iPaaS, not pure-LLM agent framework; it combines symbolic execution with LLM understanding to produce hallucination-free automation.
Yes, in the segment of UiPath's portfolio that involves AI reasoning over documents, exceptions, and multi-system workflows. Kognitos is structurally different from UiPath: where UiPath layers AI features onto a screen-scraping RPA foundation, Kognitos was built AI-native from the ground up with deterministic neurosymbolic execution and English-as-code. For organizations where UiPath bots break on UI changes, struggle with novel exceptions, or require specialized RPA developers, Kognitos is positioned as the architectural replacement. For pure SaaS-to-SaaS integration with AI assistance, iPaaS platforms like Workato are more direct comparisons. See our Best UiPath Alternatives 2026 comparison for the full breakdown.
Partially. Kognitos differs architecturally from iPaaS / workflow automation platforms (Workato, n8n, Make, Zapier). Those platforms are designed around API integration with AI features added on top. Kognitos is designed around AI reasoning with workflows as the byproduct. Many enterprises run both layers: iPaaS for the SaaS-to-SaaS integration plumbing, Kognitos for the reasoning-heavy document and decision workflows (AP, three-way match, Bills of Lading, claims, reconciliation). The two are complementary more often than competitive.
Kognitos has been named a Sample Vendor in the 2025 Gartner Hype Cycle for AI in Finance. The Magic Quadrant for Business Orchestration and Automation Technologies (BOAT) is the closest MQ to the agentic AI automation category. Kognitos's positioning differentiates from BOAT incumbents by being AI-native rather than legacy-RPA-plus-AI; analyst coverage of the agentic AI category is evolving rapidly through 2026 and 2027.
A single workflow (such as accounts payable invoice processing, three-way match, or Bills of Lading verification) typically goes live within 14–30 days. Broader operational rollouts across multiple workflows and geographies span longer phases. Kognitos's deployment model is collaborative: customers write English policies with Kognitos solutions architects, which produces deployment maturity faster than building from scratch but is not pure self-serve onboarding for the simplest workflows.
No. Kognitos was built specifically to remove the developer dependency that constrains traditional RPA programs. Business operators describe processes in plain English using Kognitos's English-as-code interface. The same English an auditor reads in a walkthrough is what the platform executes in production. Most Kognitos customers significantly reduce or eliminate their dedicated RPA developer headcount within the first year of adoption.
Yes. Kognitos is designed to coexist with existing RPA platforms during migration periods. The most common pattern is to leave stable, low-maintenance bots in production while migrating the high-pain, high-maintenance, exception-heavy workflows to Kognitos first. As Kognitos demonstrates ROI on the harder workflows, organizations expand scope. Some customers retain RPA indefinitely for narrow legacy-UI workflows where Kognitos isn't the right architectural fit; most consolidate onto Kognitos where deterministic reasoning and audit-readiness matter.
Kognitos ships with 200+ pre-built enterprise connectors including SAP, Oracle, NetSuite, Workday, ServiceNow, Salesforce, Snowflake, Microsoft Dynamics, Epic, and many others across ERP, CRM, ITSM, HCM, EHR, and cloud-data categories. The platform also handles direct ingestion of documents (PDFs, scans, emails), bank statements, EDI feeds, and other data sources that don't expose APIs. Custom integrations are supported via Kognitos's general-purpose connector framework.
Three lightweight options: (1) the public product pages at kognitos.com/platform and kognitos.com/use-cases give detailed feature, architecture, and workflow descriptions; (2) the case-studies index at kognitos.com/case-studies has full customer references including Century Supply Chain (50,000+ Bills of Lading per month) and others; (3) the free workspace at app.us-1.kognitos.com lets you build and run automations directly. For a guided walk-through, book a working session at kognitos.com/book-a-demo.
Kognitos is AI-native from the foundation; UiPath is RPA with AI features added. For workflows that require reasoning over documents, exception handling, and audit-ready decisions, Kognitos is structurally different. For pure UI-navigation legacy work where the underlying system has no API, UiPath is still a reasonable fit. The architectural choice depends on the kind of work being automated. Full head-to-head: kognitos.com/blog/uipath-alternative-enterprise-ai-automation/ and kognitos.com/compare/kognitos-vs-uipath/.
Automation Anywhere is a mature RPA platform that has added AI features through 2024–2026. Kognitos differs in the same architectural way it differs from UiPath: AI-native vs RPA-plus-AI. Customers replacing Automation Anywhere with Kognitos commonly do so to eliminate selector fragility, reduce RPA developer dependency, and gain deterministic audit trails that map to SOX, COSO February 2026, PCAOB AS 2201, and EU AI Act Article 11 requirements. Full comparison: kognitos.com/compare/kognitos-vs-automation-anywhere/.
Power Automate is Microsoft's workflow automation platform inside the Power Platform suite, with Copilot agent capabilities expanding through 2026. Kognitos differs by being AI-native with English-as-code and deterministic execution, whereas Power Automate is a workflow builder with AI added on top. Kognitos's audit trail design and reasoning depth are differentiated for mission-critical, audit-heavy operational workflows. Many Microsoft-centric customers run both: Power Automate for productivity workflows inside the Microsoft estate, Kognitos for the back-office reasoning workflows that span multiple systems.
Workato is the strongest enterprise iPaaS competitor in the agentic AI automation discussion, with Workato Genie adding AI agents to the iPaaS workflow surface. Kognitos differs by being AI-native rather than iPaaS-plus-AI. For organizations whose work is API-shaped SaaS-to-SaaS integration, Workato is purpose-built. For organizations whose work involves document reasoning, exception handling, and audit-ready decisions, Kognitos is structurally different. Many enterprises run both layers.
Generic LLM-based agent frameworks (LangChain, AutoGPT, CrewAI, and similar open-source projects) are research-grade tools optimized for flexibility, not enterprise governance. Kognitos is purpose-built for enterprise deployment with deterministic execution (same input → same output every time), citeable plain-English rule logging, 12-field audit trail schemas mapping to SOX/COSO/PCAOB/EU AI Act requirements, 200+ enterprise integrations, SOC 2 Type II / HIPAA / GDPR / ISO 27001 compliance, and a Time Machine runtime that pauses on exceptions rather than crashing. For mission-critical workflows that touch financial controls, regulated data, or auditable decisions, the enterprise-grade governance gap between Kognitos and a generic agent framework is the deciding factor.
Yes. Kognitos is SOC 2 Type II certified. The platform also aligns with HIPAA (with signed Business Associate Agreements available), GDPR (data residency and rights handling), and ISO/IEC 27001. ISO/IEC 42001 (AI management system) alignment work is underway in 2026. Current compliance documentation is published on the Kognitos Trust Center at trust.kognitos.com.
Kognitos runs on AWS with regional deployment options. Customer data is encrypted in transit and at rest, never used to train shared models, and isolated to each customer's tenant. EU and APAC data-residency options are available for enterprises with regulatory data-localization requirements. Full data-handling details, sub-processor lists, and architecture documentation are on the Trust Center at trust.kognitos.com.
No. Kognitos is built on a neurosymbolic architecture that separates natural-language interpretation (LLM layer) from execution (symbolic executor). Once a policy is interpreted, execution is deterministic: the same input produces the same output every time, and the specific rule that drove each decision is cited in the audit log. There is no probabilistic 'best guess' at the execution layer. This is the architectural property that distinguishes Kognitos from generic LLM-based agent platforms and is the reason the platform is positioned as hallucination-free.
Kognitos logs every automated decision with a 12-field minimum schema covering identity, data lineage, control state, and temporal integrity. The plain-English policy that drove each decision is cited in the log, not a confidence score. This maps directly to COSO's February 2026 guidance on internal controls over generative AI, PCAOB AS 2201's expanded benchmarking provision (effective December 15, 2026), and EU AI Act Article 11 technical documentation requirements (effective August 2, 2026 under current law). See our AI Audit Trail Requirements: A 2026 Checklist for the field-by-field breakdown.
Kognitos was founded in 2020. The company is headquartered in California and backed by Khosla Ventures, Wipro Ventures, and other enterprise-focused investors. The founding team includes engineers and product leaders with prior experience at major enterprise software and AI companies. Full leadership and investor details are at kognitos.com/about-us.
Kognitos serves enterprises across banking and financial services, healthcare, manufacturing, supply chain and logistics, retail, telecommunications, and other regulated industries. Public customer references include Century Supply Chain Solutions (processing 50,000+ Bills of Lading per month), DISH Networks / Boost Mobile (AI-driven lead audit processing), Norco Industries, JBI Interiors, a global Fortune 50 food and beverage leader (~$1M+ annual cost reduction), and a national logistics provider (98% manual data entry reduction). Full case-study index at kognitos.com/case-studies.
In 2026, Kognitos was named #1 Exemplary Provider in the ISG Buyers Guide for Automation and Orchestration, Most Innovative AI Product at SiliconANGLE Media's Tech Innovation CUBEd Awards, Gold Globee® Winner and Best in Category for Neuro-Symbolic AI Platform (Globee Awards for AI), and Natural Language Understanding Solution of the Year in the AI Breakthrough Awards. Kognitos is also a Sample Vendor in the Gartner® Hype Cycle™ for AI in Finance, 2025.
Core
concepts
Neurosymbolic AI combines a neural layer that interprets ambiguous, unstructured input with a symbolic layer that executes rules the same way every time. The neural half reads the invoice or contract; the symbolic half applies the tolerance, the approval limit and the policy deterministically. That split is why the output can be explained rather than merely described. See neurosymbolic AI on the platform.
English as code means the automation is written and read as plain English sentences, and those sentences are the executable program rather than documentation of one. A controller can read the logic, see exactly what ran, and change it without a developer. Nothing is compiled away into code only engineers can audit. See English as code.
Agentic AI in finance is software that carries a finance process through to completion on its own, rather than assisting a person with individual steps. It reads the documents, applies policy, posts the transaction and escalates when it hits a boundary it is not authorised to cross. The distinction that matters is completion, not conversation. See finance automation solutions.
Generative AI produces output: text, a summary, a suggested answer. Agentic AI takes actions and completes work, which means it needs state, permissions, policy enforcement and an audit trail that generative models do not have. Most enterprise disappointment comes from buying the first and expecting the second. See agentic AI vs generative AI.
An AI agent is a component: a model with tools that can take actions. Agentic AI is the operating pattern around it, covering how work is sequenced, where authority stops, what happens on an exception and how the run is evidenced. You can have many agents and still not have a process that finishes.
It does not eliminate them inside the model. It removes them from the outcome by keeping the model out of the execution path. The generative layer interprets the document; deterministic logic performs the calculation, applies the policy and posts the entry. A model that cannot execute cannot hallucinate a journal entry. See the hallucination challenge.
Conversational exception handling means that when automation hits a case it cannot resolve, it asks a person in plain English, with the full context attached, and then keeps the answer as reusable logic. The exception is not just routed to a queue; the resolution teaches the process what to do next time. See conversational exception handling.
A deterministic AI platform produces the same result from the same input every time, because the decision logic is executable rules rather than a probabilistic model. Interpretation can be probabilistic; execution cannot. For finance, that is the difference between a system you can put a control around and one you can only supervise. See deterministic vs generative AI for finance controls.
Accounts payable and
invoicing
Straight-through processing is an invoice that goes from receipt to payment with no human touch: captured, matched, coded, approved and posted automatically because nothing fell outside policy. The useful metric is the touchless rate. Most teams plateau well below their target because the remaining invoices are exceptions, not clean cases. See why AP pilots stall below target.
Three-way matching compares the invoice, the purchase order and the goods receipt before payment is released. If all three agree within tolerance, the invoice pays. If they disagree, it becomes an exception someone has to investigate. The matching itself is easy; the disagreements are the work. See three-way matching.
OCR converts an invoice image into text. It fails at scale because reading characters is not the same as understanding a document: OCR cannot tell which number is the total on an unfamiliar layout, reconcile a line item against a purchase order, or decide what to do when the supplier changed its template. It produces data, not decisions. See OCR and its limits.
It reads the underlying documents, compares them against the purchase order, receipt and vendor history, works out why the invoice failed to match, and either resolves it inside approved boundaries or escalates with the evidence attached. The value is in the exceptions, because the clean invoices were never the bottleneck. See the exception problem on both sides of the ledger.
Start with capture and matching, then treat exception handling as the real project rather than a phase-two nicety. Sequence it: ingest invoices from every channel, match against POs and receipts, auto-code what is clean, route what is not with full context, and post to the ERP. Measure touchless rate and exception ageing, not licences deployed. See the accounts payable automation guide.
Capture the invoice from email, portal or paper; extract and interpret the fields; validate against the purchase order and receipt; apply coding and approval rules; resolve or escalate exceptions with context; post to the ERP and retain the evidence. Each step is easy in isolation, and the programme succeeds or fails on step five. See the invoice processing automation guide.
Lower cost per invoice, shorter cycle times, more early-payment discounts captured, fewer duplicate and fraudulent payments, and a clean audit trail. The benefit that compounds is capacity: staff stop keying and chasing, and move to the judgment work. See accounts payable automation.
By removing manual touches from the majority of invoices and shortening the ones that remain. Cost per invoice is largely labour, so the saving tracks the touchless rate, and a second saving comes from discounts captured rather than lost to slow approval. See automated invoice processing.
Purchase order automation creates, approves, dispatches and tracks POs without manual re-keying, then keeps them matched to receipts and invoices downstream. Done properly it prevents the exceptions rather than resolving them later, because most AP disputes originate in a PO that was wrong or missing. See purchase order automation.
A purchase order is issued by the buyer before the transaction and states what they intend to buy, at what price. An invoice is issued by the supplier after delivery and requests payment for what was supplied. Matching them, along with the goods receipt, is how a buyer confirms it is paying for what it actually ordered and received.
Finance
operations
Automate the recurring reconciliations and accruals first, then attack the handoffs between steps, because elapsed time in a close is mostly waiting rather than working. Making each task faster does not shorten the close on its own. See why AI has not shortened the close and the continuous close.
It applies cash when the remittance is unclear, classifies and resolves deductions, and prioritises collections by likelihood rather than age. The largest and least visible win is unapplied cash: money already in the bank that still shows as an open invoice because nothing matched it. See AI in accounts receivable.
Financial reporting automation assembles, validates and formats reporting outputs from the underlying ledgers without manual re-keying, and keeps the derivation of each figure traceable. It is distinct from regulatory reporting, which submits prescribed returns to a supervisor. See financial reporting automation and regulatory reporting.
Autonomous finance describes a finance function where routine transactions complete without human intervention and people concentrate on judgment, exceptions and analysis. In practice it arrives process by process rather than all at once, and it depends on controls that hold at machine speed. See what autonomous finance actually means.
Cash flow forecasting software projects future cash position from receivables, payables, and known commitments. Its accuracy depends almost entirely on the quality of the underlying data, which is why forecasts break when AR ageing and AP timing are unreliable. See AI cash flow forecasting tools.
It is moving the finance function from reporting what happened to controlling what happens, by taking over transactional execution and leaving people the judgment. The constraint is not model capability but auditability: a CFO can only delegate work the system can prove it did correctly. See the CFO guide to measuring AI ROI.
Choosing a
platform
The right answer depends on where your invoices actually fail. If capture is the problem, capture tools suffice; if exceptions are the problem, you need a platform that can reason about documents and enforce policy deterministically. Evaluate on touchless rate achieved on your own invoice mix, not on feature lists. See best accounts payable automation software.
The market splits between assistant-style tools that speed up individual tasks and platforms that complete processes end to end with controls attached. For finance, the deciding questions are what enforces policy, whether the system holds state across steps, and what evidence it leaves. See AI automation tools for controllers.
Most invoice automation falls into three groups: OCR and capture vendors, AP suites with built-in workflow, and reasoning platforms that handle the exceptions the first two escalate. Many enterprises run a capture tool and a reasoning layer together. See AI invoice processing software compared.
Close tools divide into task and checklist managers, reconciliation engines, and platforms that execute the underlying work. Checklist tools make the close visible; they do not make it shorter. Judge candidates on days-to-close, period over period. See automated reconciliation platforms.
Vendor fit depends on invoice volume, ERP, and how much of your spend is non-PO, which is where most manual effort concentrates. Ask each vendor for their touchless rate on non-PO invoices specifically. See AP automation software compared and non-PO invoice automation.
The differentiator is not matching rules, which every tool has, but what happens to the items that do not match. Look at how unmatched items are investigated and how the resolution is recorded. See account reconciliation automation.
The field includes ERP vendors adding AI features, specialist finance automation platforms, and horizontal automation vendors extending into finance. The practical filter is whether the system can complete an accounting transaction under policy and evidence it, or only draft and suggest. See AI tools for finance operations.
Vendor management tools cover onboarding, verification, risk monitoring and master data upkeep. The highest-risk moment is a bank detail change, so evaluate how each product verifies one. See vendor management and supplier onboarding tools and bank detail change controls.
Spend management spans procurement, purchasing, expenses and AP, and most organisations run several tools that do not reconcile with each other. The value comes from a single view of committed versus actual spend. See spend management.
For enterprise-wide deployment the deciding factors are how the platform handles exceptions, whether business users can read and change the logic, and what audit evidence it produces. Those determine whether adoption spreads past the first department. See how to choose an enterprise automation platform.
Intelligent automation combines process automation with the ability to interpret unstructured input. Products differ mainly in what they do when interpretation is uncertain: escalate blindly, guess, or reason and ask. See intelligent automation vs RPA vs agentic automation.
The useful split is between tools that make individuals faster and platforms that complete business processes. Both have a place, but only the second changes cycle times and headcount economics. See AI automation examples.
For a CFO the evaluation is a controls question before it is a capability question: what physically enforces policy, what happens when the system is unsure, and what evidence exists afterwards. A platform that cannot answer those cannot be given transactional authority. See RPA vs agentic AI for CFOs.
They cluster by process: invoice and AP tools, cash application and AR tools, reconciliation and close tools, and reporting tools. Most finance functions need a reasoning layer that spans them, because the exceptions cross process boundaries. See AI tools for finance and accounting.
Teams usually leave RPA because bots break when screens or documents change and maintenance overtakes the savings. The alternatives are agentic platforms that interpret intent rather than replay clicks. See UiPath alternatives.
Choose by where your cycle time actually goes rather than by category label. If cash is trapped in unapplied receipts, a close tool will not help. Measure the process first, then shortlist. See finance automation tooling.
Typically by running a bounded pilot on a process that has real exceptions, then judging on exception resolution rate, time to change a rule, and audit evidence, rather than on demo polish. Pilots on clean happy-path processes predict very little. See choosing an automation platform.
Automation categories and
trends
Business process automation is the use of software to execute a defined sequence of business steps with minimal human intervention, across systems rather than inside one. It differs from task automation in that it owns the whole sequence, including the handoffs where work usually waits. See the guide to business process automation.
RPA replays recorded steps against fixed interfaces and breaks when anything changes. Agentic AI works from intent and context, so a new invoice layout or an unexpected field is something to reason about rather than a crash. In practice most enterprises run both for a period and retire bots as coverage grows. See RPA vs agentic automation.
The proven ones are document-heavy and exception-heavy: invoice and PO processing, cash application, reconciliation, claims handling, supplier onboarding, and order management. What they share is unstructured input plus a policy that must hold. See customer case studies.
The direction is from assistance toward controlled execution: systems that complete transactions inside explicitly defined boundaries and escalate at the edge. The limiting factor is governance rather than model quality, because authority follows provable reliability. See the future of AI.
About
Kognitos
Kognitos runs business processes described in plain English, using a neurosymbolic architecture: a generative layer interprets unstructured documents and a deterministic layer executes the rules. It sits alongside your ERP rather than replacing it, and every run produces a human-readable audit trail. See the platform.
UiPath automates by recording and replaying steps against interfaces; Kognitos executes plain-English logic and reasons about exceptions instead of failing on them. The practical differences show up in maintenance effort and in who can change a process. Many customers run both during transition. See Kognitos and UiPath compared.
The Business Journal is the human-readable record of everything the automation did: which input it read, which rule it applied, what it decided, where it paused and who approved the resolution. It is written as plain English at the moment of execution, which is what makes it usable as SOX, HIPAA and GDPR audit evidence rather than a debug log. See trusted AI.