Custom AI development · integration · business AI agents
Connect AI to your existing systems, give it only the actions it needs, and keep the evidence and approval points visible.
Binarify designs and builds focused AI applications for growing businesses. We use established models where they fit and concentrate the custom work on your workflow, data, integrations, controls and user experience.
Your accounts · your approved providers · implementation from $15,000 USD
What we build
These are solution patterns, not fixed products. Each one is adapted to the systems, data, review rules and success measure of the business using it.
Read forms, CVs, contracts, invoices or operational documents; extract required fields; check rules; show the source; and route uncertainty to a person.
ConnectsDocument store · CRM or ERP · approval queue
MeasureHandling time · correction rate · accepted documents per employee
Answer from approved policies, product information and internal documentation, cite the source and record questions the available material cannot answer.
ConnectsKnowledge base · intranet · team chat or helpdesk
MeasureTime to answer · answer acceptance · unresolved questions
Retrieve account context, prepare the next response or action, identify missing information and leave approval with the employee responsible for the work.
ConnectsInbox · CRM · helpdesk · calendar
MeasureCycle time · work completed per employee · review time
Let a model choose among specific tools and steps while permissions, limits, validation, logs and human escalation constrain what it can do.
ConnectsApproved APIs · service accounts · audit log
MeasureCompletion rate · exceptions · incorrect or reversed actions
Combine operational history with agreed business drivers to support demand, staffing, stock or prioritisation decisions, with assumptions visible to the user.
ConnectsERP or POS · data warehouse · planning workflow
MeasureForecast error · waste or shortage · planning time
Collect consistent measures from source systems, identify unusual changes and prepare commentary for a person to verify before distribution.
ConnectsOperational systems · finance data · reporting destination
MeasurePreparation time · corrections · exceptions resolved
Build only when it earns the right
A custom build is a continuing responsibility. It must create enough value to justify testing, monitoring, provider costs and maintenance as the business and models change.
the system already holds the data and provides a supported workflow that meets the need after configuration.
the requirement is common, a credible product covers it, and its ownership and integration terms are acceptable.
the capability exists but people manually move information or actions between separate tools.
a valuable gap remains, the process is distinctive, and control or integration requirements justify the investment.
The AI Impact Diagnostic documents that decision before an implementation is proposed.
How custom AI development works
A demo proves that a model can handle one example. An operational system must handle representative work, difficult cases, missing information and failures without hiding uncertainty.
Agree what enters, what a useful output contains, who may act, what errors cost and which operational measure will be taken again.
Confirm APIs, editions, permissions, identifiers, data quality, retention and the source that should govern each answer or action.
Collect normal, difficult and unacceptable examples. Define the expected result, permitted variation and cases that must be escalated.
Connect input, context, model, validation, approval and write-back for a contained portion of the live workflow.
Begin with approved users or shadow operation. Record outputs, corrections, exceptions, latency, cost and integration failures.
Train the team, measure the agreed outcome, document operation and transfer the code, configuration and access inventory.
Integration and ownership
Inference runs under your provider account and key. The workflow operates in your cloud or approved SaaS tenancy. Access uses named identities with the minimum permissions required.
The design records what information crosses each boundary, what is retained, where logs live, who can approve an action and how access is removed at handover.
Technical delivery, access and handover →Commercial scope
The project estimate separates development from continuing model, cloud, software, messaging and support costs. Human review time is included in the business case.
We start with one end-to-end path that can be evaluated and operated. Additional teams, systems or actions are staged after the first scope works.
Where we already have workflow depth
Practical questions
A custom AI solution is software designed around a specific business workflow, its data, rules, systems and users. It may use an existing model through an API rather than training a new model. The custom work is usually the retrieval, integration, controls, evaluation, user experience and operational process around that model.
Custom development is justified when a valuable requirement remains after suitable products and existing system features have been checked. The workflow should be important enough, frequent enough and distinctive enough for the expected benefit to exceed development, operation, review and maintenance costs.
That is the usual approach. We first inspect the product edition, API, permissions, data access and rate limits. Common integration points include CRMs, ERPs, helpdesks, inboxes, document stores, calendars and internal databases. Compatibility is confirmed during discovery rather than assumed.
Yes, when an agent is appropriate for the workflow. We define the tools it may call, permissions, spending or action limits, validation, logs, fallback behaviour and human approval. A deterministic workflow is often better when the steps are stable and known.
Usually the best starting point is an established model used through your own account, combined with retrieval, rules and evaluation. Fine-tuning or specialised models may be assessed when there is enough representative data and a clear advantage. We do not describe ordinary API integration as training a proprietary model.
The workflow runs in your accounts and uses your approved provider keys. The agreed source code, configuration and documentation are handed over to you. The handover pack identifies every integration and credential so access can be transferred or revoked.
The AI Impact Diagnostic starts at $2,500 USD. Implementation sprints start at $15,000 USD, with scope, milestones, fixed price and expected running costs agreed before work begins. Larger or multi-system projects may need staged delivery.
We assemble representative, difficult and unacceptable cases from the intended workflow, define what a correct result means and evaluate changes against that set. We also test permissions, integrations, failure handling and human escalation. Live operation is monitored because pre-launch tests cannot represent every future input.
We will use the first call to understand the business constraint, existing software and what would have to be true for custom work to make sense.
No confidential data or technical specification is required for the first conversation.