Pharos Production · Engineering practice
Turn document-heavy work and manual handoffs into workflows your team can review and control. Pharos Production connects your data and existing systems, then validates outputs against agreed quality, access and operating-cost criteria before rollout.
Send your workflow, data sources and expected outcome. We will clarify the scope and next steps.
Discuss your workflowAI development turns a defined business task into a working system, including data connections, application interfaces, evaluation and monitoring. We map AI use cases to available data, error tolerance and operational ownership before recommending a build. The scope identifies the inputs, the output a user will act on and the checks required for acceptance.
Discovery compares a model-based approach with rules and existing tools. The output is a scoped workflow, an evaluation dataset and an architecture proposal. If the baseline already meets the requirement, the recommendation can be a smaller change instead of a new model.
Explore this service →Integrate AI with the systems that hold the source records: an ERP, CRM, document store or internal API. Define read and write permissions separately, preserve source identifiers and send exceptions to a person who can resolve them.
Explore this service →Select retrieval, prompting or model adaptation against the same evaluation set. An architecture decision should show which requirement it satisfies, what infrastructure it needs and what would trigger a different approach.
Explore this service →AI-powered software needs a usable application around the model: permissions, review screens, source links and an audit record. Product interfaces should show uncertainty and let a user correct a result before it changes a business record.
Explore this service →Support workflows need answers grounded in approved content and a route to human assistance. AI chatbots should preserve the conversation context required for that handover while keeping private records outside unauthorized retrieval results.
Explore this service →A custom model is justified when an evaluation demonstrates a gap that prompting or retrieval cannot close economically. Compare performance on representative data before committing to training and record which examples remain outside the model's reliable operating range.
Explore this service →Use language and multimodal models where the workflow benefits from interpreting or producing unstructured content. Keep source retrieval, output validation and operating costs in the delivery scope.
A drafting assistant and a document extraction pipeline require different acceptance tests. Evaluate generated text for grounded statements; validate extracted fields against the source and a defined schema. Log the model and prompt version used for each result.
Document processing combines ingestion, retrieval and review. A reviewer needs the source passage next to the answer, a reason an item was flagged and a way to correct the record. Route unreadable files and conflicting evidence to an exception queue.
Use bounded tool access for workflows that require more than a single model response. Autonomous AI agents need explicit permissions, state tracking and a stopping rule. Advanced AI behavior belongs behind the same authorization checks as other application actions.
Separate a proposed action from permission to execute it. Require review for changes that affect customer records or money movement, and retain the inputs needed to replay the decision. Test tool failures and incomplete data alongside successful paths.
Define the allowed tools, the records they can access and the point where the workflow must stop. Retries should not repeat a completed transaction. A failed handoff needs a named owner and enough context for that person to resume the task.
An AI solution that must respect record-level permissions needs more than a model subscription. Compare the implementation and operating work required by each route. A packaged product can be appropriate when its data controls and review workflow already fit the task.
| Decision | Commissioned build | Packaged service |
|---|---|---|
| Data access | Design access around your records and permissions. | Verify supported connectors, retention and tenancy. |
| Evaluation | Maintain an evaluation set tied to the workflow. | Check whether your own acceptance tests can be run. |
| Operating cost | Budget for infrastructure, monitoring and maintenance. | Budget for licenses, usage and integration work. |
| Exit route | Specify ownership and handover in the engagement. | Check export, migration and contract conditions. |
A deterministic workflow may be cheaper to operate and easier to audit. Do not introduce probabilistic output where a task requires a zero-error guarantee on every decision. Data access restrictions and an unfunded maintenance plan can also rule out a model-based approach.
Strategic AI planning starts with ownership: who approves access to data, who accepts the evaluation result and who operates the system after launch. Data residency and incident response requirements should be resolved before selecting a hosting route.
A rollout can change how users review work and handle exceptions. Include those changes in the delivery plan, along with access management, release approvals and operating documentation. Begin with a workflow whose outcome can be measured against a baseline.
Evaluation records, audit logs and documented release decisions make controls inspectable. EU AI Act mapping is available on request. Framework alignment does not establish certification or automatic compliance; applicable obligations and legal conclusions require their own review. Specify who can access source records, which data can leave the environment and who approves release or rollback.
Check whether the data can be accessed lawfully, whether representative examples exist and whether a process owner can act on the output. A pilot should expose missing permissions or unreliable source records before those problems reach production.
Choose AI architecture against the data boundary, latency requirement and operating budget. Compare model APIs with private deployment instead of selecting a provider before the workflow is understood.
AI and ML delivery includes the data pipeline, application interfaces and monitoring around the model. Machine learning workloads also need a plan for changing source data and repeating evaluation after an update.
Our stack includes Google Vertex AI alongside other model and infrastructure options. Record why a provider or deployment route was chosen, how access is controlled and which evaluation must pass before replacing it.
Give each release an inspectable acceptance decision before expanding access. Each AI project needs acceptance criteria and an operational owner. Scope, Build, Eval and Hardening connect the work through evaluation records, monitoring instructions and a rollback procedure.
Use discovery to establish a baseline, identify disallowed behavior and prepare the first evaluation set. Define a stopping criterion for an approach that fails to improve the target workflow.
Assign engineering, data and operational responsibilities to named roles. Model work should progress alongside application integration and the review interface, so a promising evaluation result can be tested in its actual workflow.
Scope is followed by a reproducible build, shadow-mode evaluation and production hardening. The handover should include a prompt registry, evaluation results, monitoring instructions and a rollback procedure.
A development services company should state what the client receives: source code, configuration and operating documentation. Agree who handles monitoring, incidents, evaluation updates and rollback after launch. Confirm ownership, access, exit arrangements and support coverage in the engagement terms.
These anonymized production outcomes are measured against client-reported pre-engagement baselines. The client names are withheld under NDA. Named platform projects in our portfolio provide separate evidence of application and infrastructure delivery.
An EU marketplace had 12 support staff handling 8,000 tickets per week before the engagement. A custom AI agent deflected 62% of tier-1 tickets with 91% customer satisfaction, while response time on remaining tickets fell from 4.2 hours to 28 minutes. We used a 200-question evaluation set and three weeks of shadow-mode testing.
Measurement basis →For a US legal team, retrieval over 50,000 case documents achieved a three-second response time and 94% citation precision against ground truth. The client reported a 75% reduction in research time. Answers link to source paragraphs so the team can inspect the evidence.
Measurement basis →A finance operations engagement reduced month-end close from 14 days to three. Its workflow separates extraction, validation and reporting, with actions recorded for audit. The team can replay a close-cycle step using its logged inputs and tool calls.
Measurement basis →These figures are client-reported outcomes from the engagements described above. The evaluation sample and shadow period are stated where available; measurement dates and full source records are not published. Results depend on the workflow and do not establish a guaranteed outcome for another engagement.
To scale AI beyond a pilot, budget for integration and operating work alongside model usage. The following ranges are planning estimates; discovery confirms the deliverables and estimate for the actual workflow.
The pilot tier has a planning range of $15,000-$50,000 and four to eight weeks for a feasibility study, prototype and integration roadmap. Its team pairs an ML engineer with a data engineer.
Discuss this scope →The production tier lists $50,000-$150,000 and three to six months. Scope includes the model, an API layer, deployment and monitoring, with ML and backend engineering plus MLOps.
Discuss this scope →The organization-wide tier lists $150,000-$500,000+ and six to twelve months or more for multi-model infrastructure and private or hybrid deployment. A scoped estimate identifies the integrations, evaluation work and operating requirements included in the engagement.
Discuss this scope →The prices above are indicative engagement estimates. Discovery defines the data access, integrations, review requirements and operating responsibilities used in your estimate. Our cost study groups completed work by technical complexity, so its categories and ranges are not identical to these offers.
Delivery scope depends on your source records, system access and approval requirements. Use these answers to identify what must be agreed before commissioning a build.
Start with the records the workflow reads, the action it produces and the person responsible for exceptions. Test the model against examples from that task before expanding access or adding autonomous actions.
The agreed scope should name the data pipeline, application work and release controls. Monitoring, evaluation updates and support responsibilities need an owner after launch rather than being assumed to follow from a working prototype.
Tie each control to a failure that matters: unauthorized retrieval, an unsupported answer or a repeated action. A release checklist should identify the test, the acceptance threshold and what happens if it fails.
Commission a component when an existing product cannot meet a defined requirement at an acceptable total cost. Confirm that gap through a test or a documented product limit before committing to a build.
Dmytro Nasyrov, founder and CTO, leads the engineering practice. Victor Sineglazov, D.Sc., is an independent scientific advisor. Technical review addresses accuracy within its stated scope and is not an endorsement of every commercial claim. Our editorial policy explains how sources and review responsibilities are described.
Send the workflow, the systems involved and the result you want to measure. The next step is to establish whether the data and operating constraints support a scoped engagement.
The delivery total and headcount are company-reported figures. Headcount covers the company rather than the team assigned to an engagement. The linked study explains its project cohort and counting rule; projects and independently operated production systems may not be the same unit.
See the published study methodology for its period, counting rule and exclusions. Leadership profiles describe the named roles and qualifications. Case outcomes and commercial estimates have the specific limits stated beside them.
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