Agentic AI that reaches production. Priced on outcomes, not hours.
Cogninest AI is an agentic AI consulting and development partner for mid-market enterprises. We build governed AI agents that read your documents, reason over your data and act across your systems on 12+ years of applied machine learning, billed against results you can verify.
AWS Advanced TierServices PartnerGoverned by defaultFirst workflow live in 4–8 weeksTwo ways to buy AI. Both of them are broken.
SaaS sells you access and leaves implementation to you. Consulting sells you hours and is rewarded for taking more of them. AI transformation does not fit either model so we built a third one.
You pay for access
- Pays for licences, not results
- One-size-fits-all no fit to your data or workflows
- You still need services to implement and configure it
You pay for hours
- Pays for time, not outcomes slowness is rewarded
- Scope creep and budget overruns are the norm
- The knowledge walks out when the engagement ends
You pay for shipped outcomes
- Speed of software. Reusable accelerators mean weeks, not quarters.
- Rigour of services. Every deliverable is a discrete, verifiable unit of value.
- Aligned incentives. Faster delivery is our margin and your earlier result.
The full stack of AI transformation five pillars, not twelve service lines
Every layer builds on the one below it. We start wherever your foundation actually is and work up, delivering something usable at every stage. Each pillar is deployable on its own and designed to work with the rest.
Agentic Workflow Orchestration
Multi-agent systems that plan a multi-step process, call the right tools, and execute it end to end with human checkpoints only where they earn their place.
Explore pillarDocument & Data Intelligence
Agents that read, extract, validate and route unstructured documents invoices, claims, contracts, scanned forms and pipe clean structured data downstream.
Explore pillarConversational & Generative AI
LLM copilots for customers, employees and partners grounded in your own enterprise data through retrieval, with citations and guardrails on every answer.
Explore pillarApplied ML & Predictive Intelligence
Forecasting, anomaly detection, risk scoring and recommendations the analytical judgement an agent needs before it is allowed to act on anything.
Explore pillarEnterprise AI Foundations
Data engineering, cloud architecture, MLOps and governance the unglamorous layer that decides whether anything above it survives its second year.
Explore pillarOne operating layer. Every agent, governed.
Every deployment runs the same disciplined pipeline from raw enterprise data to a completed, auditable action. This is what makes an agent something your risk committee will approve rather than something your demo audience enjoyed.
Data & Systems
ERPs, CRMs, inboxes, scanned forms, call transcripts wherever the data actually lives.
Sense
NLP, computer vision and document intelligence turn unstructured input into structured signal.
Reason
Retrieval, fine-tuned models and LLMs combine to form a judgement with a confidence score attached.
Act
A multi-agent graph plans the steps, calls the tools, and executes across your systems.
Govern
Human approval on exceptions, guardrails on every action, and a full audit trail.
The question moved from “can you build an agent” to “can you run one safely”
Building a working agent stopped being the hard part. Convincing a risk committee that an autonomous system acting on production data is safe, reversible and explainable is where programmes now stall usually months after the demo impressed everyone.
Autonomy with a written boundary
Per action type, we define what the agent may do alone, what needs approval, and what it may never do as a table your risk team can read, not a line buried in a prompt.
Every decision reconstructable
Inputs, retrieved context, reasoning trace, confidence, model version, action taken, human approver. If you cannot replay a decision from six months ago, you have logs not an audit trail.
Evaluated before and after launch
A fixed evaluation set of real cases, run as a regression test on every prompt, model or retrieval change. Model upgrades are not automatically improvements, and we test rather than assume.
Built, deployed, and running in production
We would rather show you three systems carrying real traffic than a slide of logos. Each of these is a working deployment with a measurable operating change behind it.
70% better candidate matching, 50% faster search
AI-Powered Resume Search Platform
Built an LLM-powered resume search platform replacing keyword matching with semantic querying across thousands of resumes.
Read the case study15-20% reduction in electricity procurement costs
Enterprise Energy Platform
Built a predictive EMS that forecasts consumption across 96 daily half-hour slots, predicts IEX prices, and automates procurement bidding decisions.
Read the case studyAI-driven trend detection with automated subscriber delivery
Subscription Intelligence Platform
Built a subscription platform with LLM-powered trend classification, hash deduplication, PostgreSQL domain schemas, and Celery-driven email delivery.
Read the case studyFive industries we know deeply enough to be useful in week one
Rather than spreading thin across every vertical, we have concentrated where unstructured data and operational complexity make agentic AI worth the effort and where we have already shipped.
Manufacturing
Predictive maintenance, computer-vision quality agents, and supply chain orchestration.
AI agents for manufacturingCPG & Retail
Demand forecasting, trade promotion optimisation, and assortment agents.
AI agents for CPG and retailHealthcare & Life Sciences
Clinical documentation, prior-authorization, and patient-risk agents.
AI agents for healthcare and life sciencesFinancial Services
Claims adjudication, underwriting copilots, and fraud monitoring.
AI agents for financial servicesFood & Beverage
Safety and compliance agents, traceability, and formulation intelligence.
AI agents for food and beverageSee all industries
Not seeing your sector? We still might be able to help — explore the full list.
Browse allWho we are not the right partner for
Qualifying out early is cheaper for both of us than discovering the mismatch in month three. If you recognise your situation here, we will say so on the first call.
You want a proof of concept to show the board
We will build you a pilot on the way to production, but if the destination is a demo rather than live traffic, another partner will do it more cheaply.
You want the cheapest possible hourly rate
We do not sell hours, so there is no rate to compete on. If procurement is optimising for blended day rate, we will lose that comparison and should.
You want AI added to a product for the announcement
We build systems that change an operating number. If the goal is a feature badge, the scope will not survive our scoping conversation.
You need a body-shop team to sit under your architects
We take accountability for outcomes, which requires enough design authority to be responsible for them. Staff augmentation is a different business.
Field notes from production deployments
Written by the people doing the delivery, about the parts that are genuinely hard.
AI agents vs RPA: what actually changed, and what didn't
RPA is not obsolete and agents are not a drop-in replacement. The useful question is which one fits a given step of a given process here is how we decide.
The AI agent governance checklist we use before anything goes live
The buyer question has moved from who can build an agent to who can run one safely. This is the checklist we work through before a client's agent touches production traffic.
How long does it take to deploy an enterprise AI agent?
Four to eight weeks for a first production workflow is realistic. Here is what fills those weeks, and the four things that reliably turn eight into twenty.
Questions buyers ask us first
Tell us the process you would automate first.
We will come back with a point of view on whether an agent fits it, what it would take, and what it would be worth usually within one business day, and not as a scheduled sales call.
