Custom AI & Agent Development
Custom AI development for business and product teams. Build tailored AI applications, agents, and retrieval-augmented generation systems around your workflows, data, and software.
Typical timeline: 2 to 4 weeks for a pilot and 3 to 6 months for a production build, depending on scope. Deliverables: a custom AI system, evaluation evidence, deployment plan, and agreed handover. Scope, fee, and acceptance criteria are agreed before work begins.
Visión general
Custom AI and agent development is for a specific business or product need that off-the-shelf AI cannot meet. We build software that combines AI models with approved business information, rules, and tools, then test it against the tasks it must handle. Depending on the use case, the result may be an internal AI application, a customer-facing product feature, a retrieval-augmented generation (RAG) system, or a bounded AI agent.
Qué ofrecemos
We start by defining who will use the system, the task it must perform, and what a successful result looks like. The solution design may combine a language model with retrieval from approved company knowledge, business rules, APIs, agent orchestration, and human approval. We select the model, provider, hosting, and data approach around the use case's accuracy, privacy, latency, cost, and operational needs. Before release, we evaluate representative cases for answer quality, source grounding where applicable, access permissions, unsafe or out-of-scope requests, failure handling, and agreed performance targets. The build can include scoped connections to current business systems. Broader integration work, data cleaning, workflow redesign, and continuing AI operations are separate services unless included in the written scope. Before work starts, we agree the deliverables, acceptance criteria, timing, fee, code and data ownership terms, deployment responsibilities, and handover. A pilot is not a promise that every use case is ready for production; evidence from the pilot informs the next decision.
Capacidades clave
Custom AI applications and features for internal or customer-facing products
Task-focused AI agents with scoped tools and permissions
Retrieval-augmented generation grounded in approved business information
Evaluation, security controls, deployment planning, and technical handover
Valor para el negocio
Resultados tangibles que de verdad importan a tu negocio.
Build around the real user, task, and business context
Make data access, model choices, and agent actions visible and testable
Use evidence from a focused pilot to decide what belongs in production
Casos de uso ideales
For teams that need an AI feature or system designed for a particular task, user group, or product. Examples include an internal knowledge assistant grounded in approved documents, document intake and review, a customer-facing AI product feature, or an agent that uses permitted tools to complete defined steps. This service builds the AI application or agent. Broader process redesign and automation are covered by AI Workflow Automation; enterprise-wide software connections are covered by AI Systems Integration.
Resultados que impulsamos
A custom AI application, product feature, agent, or retrieval system
A documented system design, data flow, permissions, and operating assumptions
Evaluation cases and results against agreed acceptance criteria
A deployment approach suited to the approved technology environment
An agreed handover package with technical documentation and named owners
A clear decision on production release, further validation, or changes needed first
Nuestra metodología
From a defined task to an evaluated system and an agreed path to production.
We define the user, task, and acceptance criteria; map approved data and system constraints; design and build a focused solution; test it against representative cases and failure conditions; then agree whether and how it should go live. Every phase produces an artifact the client can review.
Impacto y rentabilidad
Lo que puedes esperar antes de comprometerte.
Evaluation
Agreed test cases
Set acceptance criteria around real tasks and relevant failure conditions.
System design
Use-case specific
Choose models, data access, tools, and deployment based on constraints.
Release decision
Evidence before launch
Review results, known limits, owners, and remaining risks before release.
Opciones de colaboración
Con plazos acotados, responsable asignado y control de costes.
AI Prototype or Pilot
2 to 4 weeks, scope dependentBuild a focused version of one AI feature or agent, then test it against agreed cases before deciding whether a production build is justified.
Entregables
A scoped working application or agent, evaluation findings, known limits, and a recommendation for the next step.
Production Build & Launch
3 to 6 months, scope dependentBuild, evaluate, and prepare the agreed AI system for production use. Final scope and timing depend on requirements, data, integrations, and deployment constraints.
Entregables
The agreed AI application or agent, scoped system connections and controls, evaluation evidence, deployment preparation, documentation, and handover.
Run & Scale
Ongoing, scoped monthlyContinue evaluation, maintenance, and agreed improvements after launch as models, data, and business needs change.
Entregables
Agreed monitoring, evaluation refreshes, model or prompt updates, maintenance, issue response, and enhancements.
Illustrative build example
Example onlyExample: an internal policy assistant with controlled document access
- Approved knowledge: retrieve answers only from selected policy documents, respect each user's permissions, and show the supporting source where appropriate.
- Bounded behavior: answer when evidence is present, say when it is missing, and route uncertain or sensitive questions to a person.
- Evaluation: test representative questions, outdated or conflicting documents, unauthorized requests, and failures before release. This is an illustrative example, not a client project or measured result.
Riesgo y cumplimiento
Set the data and tool permissions the system needs, and limit actions to the approved task.
Require human review for consequential actions or cases outside the agreed confidence and safety limits.
Test representative cases, edge cases, access rules, failure handling, and agreed performance criteria before release.
Agree hosting, privacy, logging, retention, monitoring, escalation, and ownership responsibilities for the project scope.
¿Encaja contigo?
Claridad antes de comprometerteBuen encaje
- You can describe the user, business task, and result the system should support.
- The data, systems, and subject matter experts needed for design and evaluation are accessible.
- You need a custom AI application, feature, retrieval system, or task-focused agent rather than an off-the-shelf tool.
No encaja
- You need an unbounded agent to make consequential decisions without human oversight.
- The main need is to clean data, redesign a workflow, or connect many existing systems before an AI build can be scoped.
- You expect guaranteed accuracy or business returns before the use case and test criteria are defined.
Después de la colaboración
No te dejamos soloAgree documentation, system ownership, deployment roles, and named maintainers before handover.
Use Run & Scale for agreed monitoring, maintenance, evaluation, and improvements after launch.
Revisit test cases when models, data, permissions, or business requirements change.
Entregables clave
Use-case brief with users, task boundaries, requirements, and acceptance criteria
Technical design covering model, retrieval, tools, data flow, permissions, and deployment assumptions
Custom AI application, product feature, agent, or retrieval system within the agreed scope
Scoped integrations with the approved systems, APIs, or data sources
Evaluation cases and findings for answer quality, groundedness, access, safety, and reliability
Deployment and operations plan with monitoring, fallback, escalation, and human-review requirements
Cómo trabajamos
Six steps from the business task to an evaluated build and handover.
Qué esperar
- Agree on the user, task, data, constraints, and acceptance criteria.
- Review the design, test evidence, risks, and open decisions as work progresses.
- Receive the agreed system, evaluation results, and deployment or handover plan.
Define the task
Agree who will use the system, what it should do, what it must not do, and how the result will be evaluated.
Review data and constraints
Identify approved data, user permissions, existing systems, security requirements, and the people who must review important decisions.
Design the system
Choose the model and hosting approach, retrieval method, tools, integrations, controls, and fallback behavior for the use case.
Build a focused version
Implement the agreed application or agent around a narrow, useful task, with the interfaces and controls needed to evaluate it.
Evaluate and improve
Test representative and edge cases for quality, grounded answers, access permissions, unsafe actions, reliability, latency, and cost. Resolve material issues or record them as release limits.
Prepare release and handover
Review acceptance results, confirm deployment responsibilities, document the system and ownership terms, then plan monitoring, support, or the next build phase.
Preguntas frecuentes
Haz clic para ver las respuestas01What kinds of custom AI systems can you build?+
02How do you decide between RAG, an AI agent, and standard software?+
03Can the system use our company data and existing software?+
04How do you test an AI application or agent before launch?+
05Does a custom AI build include production deployment?+
06How long does custom AI development take, and what does it cost?+
07How is this different from AI workflow automation or systems integration?+
08Who owns the system, and what happens after launch?+
Have a specific AI system in mind?
Tell us who will use it, what it needs to do, and which data or software it must work with. We will help define a realistic first scope, evaluation plan, and route to production.