An existing product may fit.
If available software meets the requirement, connects to the systems involved and can operate under your policies, it may be the better choice. Compare that option before committing to custom work.
Custom AI development
Build the AI system your business actually needs.
An assistant that can use your product knowledge. A voice platform built around your training program. Automation that works through a process and knows when someone needs to approve the next step.
ALLTIPLY connects the model to the data, software and operating rules the work depends on. We agree how to evaluate the system before treating a successful demonstration as a finished result.
Request a project reviewTell us what needs to change. We’ll review the context and discuss the next step with you.
Need the system to run in a defined data boundary? Explore private AI deployment and operation. Both can be part of one engagement.
Start with the work, then decide what to build.
Custom development is worth considering when the workflow, data, controls or product behavior cannot be met adequately by existing software. The review needs an owner for the result, usable source information and a clear account of what people need to do differently.
A custom system may be needed.
A consequential process may depend on organization-specific knowledge, several connected systems, unusual exceptions or controls that a packaged product cannot support. Those requirements give the build a concrete purpose.
Compare the smallest useful change before commissioning a build.
If the problem is a missing owner, inconsistent source information or a difficult handoff, fix that first. If an existing product supports the workflow and your access rules, compare its integration effort and operating cost with custom work. Build when a specific requirement remains unmet.
Try one representative workflow with normal cases and exceptions. Agree what a reviewer must be able to check, what the system may do and how people continue when it fails. Include implementation, licenses, review effort and support in the comparison.
The single-rebuild analysis shows why the failure mechanism matters; it does not establish that every stalled system needs rebuilding. For a wider cost comparison, use the AI cost-planning guide.
Request a review of the workflow and build-or-buy decision. Describe the unmet requirement and the alternatives you have considered.
Systems we can work through with you.
Knowledge assistants
Help employees or customers use approved information, inspect the sources behind an answer and reach a person when the system cannot answer adequately.
Explore enterprise knowledge assistantsVoice practice systems
Connect practice conversations, transcripts, grading and reviewer corrections to the training program and the people responsible for it.
Explore voice AI training systemsConversation intelligence
Turn conversations into reviewable facts tied to source quotes. Define what can be counted and which decisions the information should support.
Explore conversation intelligenceWorkflow automation
Connect models, tools and approvals across a process. Define exceptions, permissions and human review before allowing the system to act.
Explore AI workflow automationExplore proposed custom AI workflows.
Explore 20 proposed solution blueprints. Three now include bounded local synthetic demonstrations for permission-aware retrieval, invoice matching and approval-gated updates, with code, fixtures and execution results. The full workflows, larger scenarios and demonstrations for the other 17 remain proposed. No measured client business results are reported.
Agree the scope and the acceptance questions.
The proposal should name the decisions, deliverables and responsibilities for the engagement. A project review establishes the context; deeper architecture and delivery work follow under an agreed scope.
Define the work and its owner.
Map the current process, people, exceptions and baseline. Establish what a useful result looks like and compare build, buy and process changes.
Test the approach against representative work.
Review source coverage, access permissions and integration behavior. Agree evaluation inputs, output quality, response expectations, cost and acceptable failure handling.
Build and review the connected system.
Implement the agreed application and integrations. Test permissions, approval paths and failure recovery alongside model behavior, and document unresolved gaps.
Prepare the people and operating environment.
Agree deployment checks, user support, monitoring, escalation and change control. Launch readiness depends on the acceptance criteria and a named operating owner.
Decide who runs it after launch.
Deployment and operation
Your infrastructure, your cloud account or a managed environment can be considered alongside the application. Define where prompts, documents, outputs, logs and backups go, and which external services and support access are permitted.
Compare private AI deployment optionsOwnership and handoff
Name the code and data deliverables, evaluation assets, documentation and runbooks in the agreement. Third-party licenses and service accounts affect what can transfer. Your team, ALLTIPLY or a shared arrangement can operate the system under a defined scope.
Read the operating and handoff considerationsScope and cost depend on source readiness, access, integration depth, usage, deployment requirements and operating coverage. Estimates should use those inputs and the work actually included.
Discuss the work you need to change.
Describe the problem, the systems involved and what is preventing progress. You do not need a finished specification. We’ll review the context and discuss whether ALLTIPLY is the right team to help.
Request a project reviewPrefer email? solve@alltiply.com
