Choose technology around a well-designed workflow.

Kepler26 helps medcomms agencies and pharma teams move from persistent workflow friction to practical, adopted AI-enabled systems.

We stay close to the work from diagnosis through implementation, so the strategy does not disappear into a deck.

From first move to production

Start clearly. Build as far as the workflow requires.

Start with one workflow and the smallest engagement that can answer the next important question. A validated design can continue into staged production engineering without changing the accountable consulting relationship.

01

Workflow DiagnosticA focused review of one workflow to find wasted effort, risks and the best place to begin.

Find and quantify one source of friction.

Duration
Typically 2-3 weeks
Who is involved
An executive sponsor, workflow owner and 3-5 people close to the work
What we do
Interviews, a map of how work happens today, analysis of wasted effort, a measurement plan and ranked next steps
What you receive
A map of today's process, a list of problems and risks, starting measurements and a focused plan
Success looks like
A clear build, redesign or stop decision
Commercial model
Fixed price for an agreed scope
Discuss this engagement
02

AI Workflow BlueprintA detailed plan for how people, AI and rules-based systems should work together before anything is built.

Design the target workflow before committing to a build.

Duration
Typically 3-5 weeks
Who is involved
Workflow experts, accountable reviewers and relevant operations or technology partners
What we do
Decide which tasks belong to people, AI or rules-based systems; plan safeguards and unusual cases; define needs; compare solutions
What you receive
A design for the improved workflow, clear responsibilities, safeguards, requirements and a plan for putting it into practice
Success looks like
A workflow design that is ready to build
Commercial model
Fixed price for an agreed scope
Discuss this engagement
03

Prototype SprintA short, focused build that tests the hardest assumptions with real users before a larger investment.

Build enough of the workflow to test the hard assumptions.

Duration
Typically 4-8 weeks
Who is involved
A workflow owner, representative users, accountable reviewers and a technical contact
What we do
Build a focused test version, define success measures, test with users, check safeguards and plan next steps
What you receive
A working test version, test results, user feedback and a recommendation to continue, change direction or stop
Success looks like
Evidence for whether and how to proceed
Commercial model
Fixed price for each agreed stage
Discuss this engagement
04

AI Workflow & Readiness AssessmentA practical assessment of workflow value, required information, available technology, architecture, governance and human oversight before implementation.

Decide what is ready, what is missing and what to do first.

Duration
Typically 3-5 weeks
Who is involved
An executive sponsor, workflow owners and relevant operations, technology, privacy or security partners
What we do
Prioritize workflow opportunities, map information sources, assess available systems, define access and review boundaries and compare implementation options
What you receive
An opportunity map, information-source map, technology assessment, security and privacy considerations, governance needs and an implementation roadmap
Success looks like
A practical architecture and prioritized path to implementation
Commercial model
Fixed price for an agreed scope
Discuss this engagement
05

Production Build & Enterprise DeploymentA staged engineering engagement that turns a validated workflow or prototype into a secure system integrated with the organization's approved environment.

Move from validated concept to production operation.

Duration
Defined after architecture, integration requirements and delivery stages are agreed
Who is involved
The workflow owner, Kepler26 engagement lead, solution architect, relevant engineers and security specialists, and client technology stakeholders
What we do
Technical architecture, application and AI engineering, integrations, identity and access, infrastructure, security engineering, automated testing, deployment pipelines, logging, monitoring and user acceptance
What you receive
A production system, deployment and operating documentation, tested integrations, monitoring approach and an agreed path for ongoing support
Success looks like
A production system that solves the intended workflow problem within the client's technical and governance environment
Commercial model
Defined by architecture, scope, integration requirements and agreed delivery stages
Discuss this engagement

Supporting capabilities

Use what the workflow requires.

An engagement can draw on the capabilities below without forcing every workflow into the same technology or delivery plan.

01

Workflow diagnosticsA close look at how work really happens to find wasted time, risks and opportunities to improve it.

Find where the time actually goes.

We map how work actually happens, including shadow systems, duplicate work, manual handoffs, review loops, workarounds and the tasks experienced people have quietly absorbed.

  • Map of how work happens today
  • List of wasted effort and risks
  • Starting measures for time and capacity
  • Ranked plan for what to fix first
02

AI workflow redesignRedesigning a process so each task is handled by the right person, AI assistant or rules-based system.

Redesign the work before choosing the tool.

We determine what should be removed, what can be automated, where AI can assist and where human judgment must remain. The result is a target workflow with clear ownership, controls and handoffs.

  • Design for how work should happen
  • Clear roles for people, AI and rules-based systems
  • Routes for review and unusual cases
  • Requirements for putting the design into practice
03

Enterprise solution architectureThe system-level design connecting workflow requirements to applications, models, data, integrations, permissions, deployment and operations.

Fit the architecture to the organization, not the organization to the tool.

We define system boundaries, data flows, model and provider choices, application components, APIs, identity, permissions, deployment models, observability, human approvals, resilience and security requirements before engineering begins.

  • Current-state and proposed architecture
  • Application, integration and data-flow design
  • Authentication and permission model
  • Deployment, observability and scale requirements
04

Enterprise AI & software developmentMultidisciplinary engineering for secure internal applications, AI systems, workflow platforms, integrations and enterprise automation.

When the workflow requires more than configuration, we can build it.

Some problems need an automation. Others need a production application, multi-agent workflow, knowledge platform, integration layer or purpose-built system. Kepler26 engineers the simplest level of solution that can reliably do the work.

  • Production applications and administration interfaces
  • AI agents, retrieval and document-processing systems
  • APIs, middleware and workflow engines
  • Automated testing, CI/CD and operational documentation
05

Enterprise integration & secure deploymentConnecting and releasing the system within approved platforms, access boundaries and operating environments.

Make the solution part of the environment where work already happens.

We integrate with supported enterprise platforms, APIs, databases and cloud infrastructure, then plan identity, environment configuration, testing, deployment, logging and monitoring with client technology and security stakeholders.

  • Approved-system and API integrations
  • Identity and access integration
  • Deployment pipelines and environment configuration
  • Production release and operating handoff
06

Evidence and content systemsTools and processes that help teams find evidence, connect it to claims and reuse approved content safely.

Make knowledge easier to find, trust and reuse.

We create workflows for finding literature, extracting evidence, keeping claims linked to sources, reusing content and preparing material for review, with scientific quality treated as a core design requirement.

  • Tools for recurring searches and monitoring
  • Systems that link claims to evidence
  • Automated reference and annotation tools
  • Processes for reusing approved content
07

Adoption, training and governancePractical training, rules and oversight that help people use a new workflow safely and consistently.

Make the better workflow the normal workflow.

People need to trust the tools they use and understand how to use them safely. We help teams adopt the new process, understand its limits and establish practical controls for regulated work.

  • Training tailored to each role
  • Clear operating instructions
  • Rules for when people must review the work
  • Measures of use and adoption
08

AI strategy and cost controlChoosing the simplest suitable technology and managing its quality, usage and cost.

Use the right capability at the right cost.

We match each task to the simplest capable approach, whether that is a smaller model, a rules-based system or no model at all. We also help teams control usage, evaluate quality and avoid building an expensive solution to a small problem.

  • Comparison of model and tool options
  • Plan for managing costs and usage
  • Tests for whether results are good enough
  • Guidance on building or buying
09

AI workflow architecture and security reviewA pre-build review of workflow data flows, providers, permissions, human approval points and deployment architecture.

Give business and technology stakeholders something concrete to review.

We map the proposed workflow, information sources, integrations, providers and permissions before development. Where practical, we design around technology the client already owns and governs, then document where additional infrastructure is genuinely required.

  • Current-state and proposed architecture
  • Workflow, data-flow, API and integration diagrams
  • Model, provider, authentication and permission inventory
  • Agent scopes, human approvals and audit requirements
  • Deployment architecture, implementation plan and engineering estimate

Interactive engagement builder

Find the right first move.

Five quick choices turn a generic enquiry into a focused starting point. The recommendation is directional; the workflow still comes first.

The first step

Choose one workflow worth fixing.

You do not need an enterprise-wide AI transformation to start recovering meaningful capacity.

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