AI implementation, integration, custom agents, private LLM deployment and process automation for US businesses, planned around the work your teams need to improve. Start with a free consultation.
Begin with an opportunity, an operating challenge, or an existing initiative that needs direction. We define the engagement around the outcome and connect the advisory, engineering, and adoption work required to support it.
No AI services match these filters.
Identify where AI fits your business and which opportunities deserve investment. We assess the work involved, the available information, and the expected value alongside implementation and operating costs. The resulting priorities give leadership a basis for deciding what to investigate, test, or build.
Establish whether the information needed for the proposed application is available, usable, and appropriately controlled. We examine source quality, permissions, knowledge ownership, and integration dependencies. That assessment identifies the preparation required for a meaningful evaluation and a workable implementation.
Define how models, data, applications, deployment choices and operating responsibilities fit together.
Define who owns an AI application, what it is permitted to access or do, and where human review is required. We connect those decisions to implementation through access controls, approval points, monitoring, and records of relevant activity. Governance becomes part of how the application operates.
Turn a defined use case into an application connected to the systems and information your team uses. We bring software development, data connections, and evaluation into the implementation scope. The work covers how people interact with the application, how outputs are checked, and how exceptions return to the responsible team.
Design supervised agents with defined tasks, permitted tools and human approval points.
Connect repeatable work across systems with clear rules and responsibilities. We examine process steps, approvals, handoffs, and exceptions before implementing automation. Where a task requires interpretation rather than a fixed rule, we assess whether an AI component adds value within that workflow.
Begin with an advisory consultation about workloads, data boundaries, deployment choices and operational ownership.
Develop language-model solutions, assessing managed, private or local deployment against the use case.
The strongest starting point for AI is a clearly defined task: what people need to accomplish, the information they use and where the current process falls short. Cognativ examines that task alongside the conditions that make AI useful: accessible data, dependable system connections, clear responsibilities and a way to measure the result.
Not every step needs a model. Some work is better handled by existing software, a rules-based workflow or a better connection between applications, so we weigh the cost of a wrong output against the effort saved before recommending an approach.
The work to improve, the people involved, and the current constraint.
Relevant data, access permissions, and connections to existing systems.
Output quality, review effort, operating cost, and the intended improvement.
Match the first engagement to where your initiative stands. Advisory work compares opportunities and clarifies constraints; implementation connects a defined use case to the systems and people who use it; infrastructure follows the workload, data and deployment model; and adoption defines how users report problems and how exceptions are reviewed.
Created by Cognativ founder Ali Davachi, RAPID connects Research, Analyze, Plan, Implement, and Decide. We use it to manage the engagement from the initial business question through implementation and review, keeping technical choices connected to the work the organization needs to improve.
Understand the current workflow, the people involved, and the information available. Examine where the proposed application could help, what could prevent it from working, and how its usefulness will be assessed. Unresolved questions become part of the investigation and evaluation plan.
Define the implementation scope, responsibilities, and evaluation criteria. Test the approach with relevant tasks and review the results with the people who will use and manage it. Use that evidence to decide what to refine, introduce into operations, or expand.
Start with one business task and agree how success will be measured before building. The first evaluation uses ordinary and difficult cases, includes the team that will use the workflow and names who owns changes once the solution is live.
Clarify the intended users, inputs, outputs, and boundaries of the application. Agree on how the team will assess quality, effort, cost, and the handling of exceptions against the current way of working.
Establish the data connections, access permissions, review points, and operating responsibilities needed for delivery. Plan the introduction with the teams affected so technical implementation and adoption move together.
Discuss the opportunity, review the proposed approach, and agree on the work before the engagement begins.
Share the task, workflow, or AI opportunity you want to explore.
Discuss the business objective, existing systems, available data, and current constraints.
Review our understanding and the proposed consulting or implementation approach.
Confirm scope, responsibilities, timing, and fees before the RAPID-managed engagement begins.
Explore Cognativ work on connected logistics workflows, healthcare data services, expert methodology automation and order integration. These cases describe software and data work in their own contexts.
The projects illustrate process, information and delivery decisions relevant to digital operations.
Cognativ worked with a logistics firm on a digital platform and a portal for connected operational workflows.
Explore the logistics case
Cognativ helped a healthcare payer reposition its data services and rebuild its platform for normalization and analytics.
Explore the healthcare data case
Cognativ turned a senior-partner-led consulting methodology into a scalable custom software platform.
The solution reduced engagement time by 50%, improved delivery quality, and increased capacity by 200x so junior consultants could execute premium engagements independently.
Explore the 200x Scaleup
An order can pass through a storefront, payment service, inventory system and fulfillment operation. In Cognativ's omnichannel retail engagement, integration automation and observability addressed failures across that connected order lifecycle.
When reviewing a commerce platform, identify where an order changes state, which application owns each update and how teams recover an exception. That makes it possible to distinguish a storefront problem from an integration or fulfillment problem.
Explore the retail order integration case study.
Tell us what your team needs to accomplish and where the current process creates friction.
Share the systems involved, the information available, and any tools or evaluations already underway. We will discuss the opportunity and the appropriate next step.
Understand the scope, the working relationship, and how to start.
AI Services brings together the advisory, engineering, integration and adoption work involved in an AI initiative. AI Consulting focuses on the decisions that shape an engagement, including use-case selection, readiness and implementation priorities. The right scope depends on what your team needs.
We examine the task. Defined rules and repeatable actions may suit conventional automation; work involving interpretation may benefit from AI. Some workflows combine both, with review points appropriate to the task.
Yes. A focused scope allows the team to evaluate a specific application, its dependencies, and its operating requirements before deciding whether to extend it.
Yes. The engagement can include application development, integration, evaluation and adoption support. We agree on the work and responsibilities before it begins.
We start with the information required for the selected use case. The assessment identifies relevant gaps in quality, access, and ownership so preparation is tied to the application rather than an undefined data-cleanup program.
We assess their capabilities, integration options, permissions, and fit with the workflow. That helps determine what can be retained, connected, configured, or extended.
We agree on evaluation criteria before implementation and compare results with the current process. Relevant measures can include output quality, completion time, review effort, exception rates, and operating cost.
The use case, data preparation, integrations, evaluation requirements and delivery support shape the engagement. We discuss timing and fees before agreement.
