Last reviewed August 15, 2026
Pharos Production delivers custom AI agent development services for enterprises and startups.
5.0/5 · 108 reviews
4.9/5 · 147 reviews
5.0/5 · 27 reviews
282 published client reviews across 3 platforms. Counts are summed; reviewers may appear in more than one source.
Profiles checked . Glassdoor: 46 employee reviews, 5.0/5, reported separately.
See the products we built and the engineering work involved. Each case describes its scope and delivery context.
Pharos Production collaborated with a taxi aggregator platform to develop a high-load ride-hailing application that connects passengers and drivers in real time. This platform consolidates various fleets and independent drivers into a single system, ensuring quick ride matching, live tracking and transparent pricing. Built on a cloud-native infrastructure, the solution offers low-latency interactions, reliable trip processing and scalability for operations at the city and regional levels.
Pharos Production has partnered with Sagas to create a location-aware social platform that enables users to capture, publish and explore geo-located timelapses over time. This system combines real-time data ingestion, large-scale media processing and map-centric discovery to transform physical locations into dynamic digital stories. Leveraging cloud-native infrastructure and event-driven architecture, Sagas allows users to document urban changes, natural evolution and personal moments tied to specific places. The result is a scalable social network where time and location are central to content discovery.
Pharos Production has partnered with Pulse to create a community-driven social network that connects users with local stores through challenges, engagement activities and real-world prizes. This platform transforms everyday local interactions into interactive experiences, enabling users to earn rewards from participating merchants. Built on a scalable, event-driven architecture, Pulse facilitates real-time interactions between users and businesses and supports rapid growth across cities and regions.
Practice led by Dmytro Nasyrov
Founder and CTO
23+ years in custom software development. Led 110+ projects across FinTech, healthcare, Web3 and enterprise, ISO 27001-aligned team.
Technically reviewed by Victor Sineglazov, D.Sc.
Independent AI Scientific Advisor
Professor, Artificial Intelligence Department, Igor Sikorsky Kyiv Polytechnic Institute. Head of the Aviation Computer-Integrated Complexes Department, Kyiv Aviation Institute.
Reviewed for technical accuracy on August 15, 2026. Not an endorsement of any commercial claim on this page.
Agent runtime architecture
The loop every production agent we ship runs on: router, planner, tools, evaluator, guardrail and audit log. Evaluator can retry via the planner or escalate to a human.
Compare agents and direct model calls on the same workflow. Agents support tool selection and recovery across multiple steps. A direct call has fewer moving parts for a single completion. The choice depends on measured quality, latency, allowed actions and operating cost.
| Factor | Custom AI agent | Direct LLM API call |
|---|---|---|
| Reasoning steps | Multi-step planning, tool use, self-correction | Single completion; no tool use unless wrapped manually |
| Tool integration | Native tool calling with structured output validation | Manual function-calling wrapper required for each tool |
| State management | Conversation memory, intermediate state, audit log | Stateless; you manage history |
| Latency | Measure the complete tool and model loop on your workflow | Measure the selected model and workload |
| Cost per request | Include model calls, tool use, retries and hosting | Depends on model, context size and serving configuration |
| Determinism | Lower; agent can choose different paths | Sampling settings alone do not guarantee identical outputs |
| Eval complexity | High; need to test multi-step reasoning paths | Low; test single input/output pairs |
| Best fit | Multi-step workflows, tool orchestration, complex Q&A, copilots | Classification, summarization, structured extraction, single-shot generation |
AI agent projects follow Pharos Verified Delivery with agent-specific gates: discovery defines goal, tool surface and evaluation set; build runs shadow-mode evaluation against human baselines; production readiness includes guardrails, audit logging, drift detection and rollback procedures; support includes prompt versioning, monthly eval refresh and ongoing drift monitoring.
Week 0
2-4 weeks
82% on-schedule with discovery
Week 2-4
2-week sprints
Transparent progress tracking
Week 8-24
ISO 27001 aligned
Launch day
Ongoing
In progress
Continuous improvement
Pharos Verified Delivery applied to 110+ production applications since 2013
Three agent engagements where we measured accuracy against human baselines before routing real traffic. Client names anonymized under NDA with industry, region and engagement stage preserved. Metrics verified against client telemetry and post-launch production instrumentation.
12 full-time agents handling 8,000 tickets per week. Average response time 4.2 hours. Tier-1 questions consumed 70% of agent capacity.
Custom AI agent deflects 62% of tier-1 tickets with 91% customer satisfaction. Agents now focus on complex cases. Response time on remaining tickets dropped to 28 minutes.
We started with a 200-question evaluation set built from real ticket history, ran the agent in shadow-mode for 3 weeks against human responses and only routed live traffic once accuracy beat the human baseline on tier-1 categories.
Junior attorneys spent 6-8 hours per case reviewing precedent documents. Inconsistent citations across the team.
RAG system over 50,000 case documents with 3-second response time. Citation precision 94% verified against ground truth. Junior attorney research time cut by 75%.
Built on a private vector store with citation tracking back to source paragraphs. Every answer ships with a verifiable footnote so partners can audit any response in under 30 seconds.
Manual orchestration of 6 internal tools for finance ops. 14-day month-end close. Three full-time analysts.
Multi-agent system with finance specialist, data extractor, validator and reporter. Month-end close in 3 days with full audit trail. Analysts redeployed to higher-value forecasting work.
Each agent has a narrow tool surface and a structured handoff protocol. Every action is logged with the full prompt, intermediate state and final tool call, so finance can replay and audit any close-cycle step on demand.
Client names anonymized under NDA. Full case studies at /cases/.
We decline roughly 30% of RFPs we receive. Forcing a bad fit costs both sides 3-6 months and damages outcomes. Here is how we think about scope:
Use an agent when the next action depends on information discovered during execution. Compare a direct model call for a single completion and a deterministic workflow for stable rules. Measure task quality, latency and total operating cost before committing to orchestration.
Pharos AI agent portfolio
Ranges we consistently see across 12+ production AI agent engagements.
82-94% end-to-end task completion on mature agents after 4-8 weeks of eval iteration; below 75% signals prompt and tool design needs rework.
Build duration depends on tool integrations and evaluation scope; use the MVP and multi-agent planning ranges above for budgeting.
$2.5k-$15k per month in inference and tool spend for mid-volume agents; scales to $20k-$60k at high-volume use.
3-15 tool calls per task on mature agents; outlier runs at 50+ calls flagged for investigation within 24 hours.
Prompt or tool-set changes ship in 2-4 hours after eval parity check; major architecture changes 1-2 weeks.
Test the tool surface and failure paths on the workflow you intend to automate.
Check tool selection, arguments and returned status on a versioned task set. Include duplicate requests, timeouts and partial failures.
Compare a single agent with a workflow or specialist agents using the same tasks. Record the quality gain alongside added latency and maintenance.
Define which actions require approval, which can be reversed and which must be blocked. Set a tool-call budget and test that it stops loops.
The weights below are an illustrative starting point. Agree task-specific thresholds, critical failures and evidence owners before testing. NIST and OWASP do not set these weights.
Task success, tool accuracy, error recovery
Use held-out tasks representative of expected traffic. Record successful tasks over attempted tasks and examine failures by workflow and tool.
Permission checks, refusal behavior, rollback
Test forbidden actions and adversarial inputs. Define release-blocking failures separately from the aggregate score; a weighted average must not hide an unsafe action.
Cost per task, P95 completion time, tool-call budget
Measure under the expected concurrency and model configuration. Set budgets from the user workflow and test timeouts and loop termination.
Trace coverage, version IDs, retention policy
Capture the model, prompt and tool versions needed to replay failures. Apply access controls, redaction and a retention period agreed for the data being processed.
Production post-mortem
A customer service agent deployed in July 2025 hit a corner case where the reply-send tool returned ambiguous success/failure status. The agent interpreted the ambiguous response as "try again" and resent the same email 340 times before the tool-call budget tripped. Caught when the customer raised a ticket about mailbox flooding.
Tool-call budget and deduplication key now mandatory on every stateful tool. Idempotency keys added to email and message-send actions. Tool-response ambiguity eliminated via explicit status codes.
Published record
Technical articles, comparison guides and methodology deep-dives we write from our own delivery experience.
Technology platforms and company references shown for identification. A logo does not establish a commercial partnership or endorsement.
16 references
Consensys
Gate.io
Coinbase
Ludo
Core Scientific
Debut Infotech
Axoni
Alchemy
StarkWare
MARA Holdings
MicroStrategy
Nubank
OKX
Uniswap
Riot
LeewayHertz
Founder and CTO Pharos Production
I design and build reliable software solutions - from lightweight apps to high-load distributed systems and blockchain platforms.
PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.
13 years in architecture of great software solutions tailored to customer needs for startups and enterprises
23 years of practical enterprise customized software production experience
Lecturer at the National Kyiv Polytechnic University
Doctor of Philosophy in Artificial Intelligence
Master's degree in Computer Science, completed with excellence
Master's degree in Electronics and precision mechanics engineering
Pharos Production scopes engagements in three tiers, AI discovery and PoC, Production AI system and Enterprise AI platform, with typical budgets from $15,000 to $500,000+ depending on scope and complexity.
Feasibility study, prototype on your data and integration roadmap in four to eight weeks.
Full model development, API layer, cloud deployment and MLOps with monitoring.
Multi-model architecture, custom data infrastructure, compliance and hybrid or on-prem delivery.
Prices vary based on project scope, complexity, timeline and requirements. Hourly rates range from $50 to $99 depending on role and seniority. Contact us for a personalized estimate.
Need extra hands on your software project? Our developers can jump in at any stage - from architecture to auditing - and integrate seamlessly with your team to fill any technical gaps.
Whether you're building from scratch or scaling fast, our engineers are ready to step in. You stay in control, and we handle the code.
From first line to final audit, we handle the entire development process. We will deliver secure, production-ready software, while you can focus on your business.
We select the stack for your product, integrations and operating requirements.
Selected badges from Clutch, GoodFirms and The Manifest. Open the issuer profiles to review categories and dates.
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.
We'll create a complete software solution that is custom-made to meet your exact specifications.
Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.
Map SEO questions to permitted tools and dated source data. Use a test matrix for project access, missing metrics and expensive live collection.
AI agent development costs range from $10,000 for a simple chatbot to $300,000+ for enterprise multi-agent systems. The final cost depends on agent complexity, number of integrations, model selection, security requirements and deployment infrastructure. Based on 110+ projects delivered since 2013, Pharos Production provides transparent cost estimates within 48 hours of receiving requirements. This guide […]
AI agent architecture patterns are reusable design blueprints for building autonomous AI systems that reason, plan and execute tasks. Just as software engineering has MVC and microservices patterns, AI agent development has established patterns that solve common challenges: how agents decide what to do next, how they access tools, how they maintain memory and how […]
Quick Comparison: Build vs Buy Factor Build Custom Buy/Platform Time to first version 8-16 weeks 1-2 weeks Annual cost (enterprise) $100K-$300K dev + $20K infra $50K-$200K licensing Flexibility Full control Vendor roadmap Integration depth Custom APIs, deep system access Pre-built connectors IP ownership Assigned to you in writing under the contract Vendor owns core logic […]
Multi-agent systems represent a fundamental shift in how enterprises build AI. Instead of relying on a single monolithic model to handle every task, multi-agent architectures deploy specialized AI agents that collaborate, delegate and coordinate to solve complex business problems. This guide covers the architecture patterns, frameworks, coordination strategies and production deployment lessons that engineering teams […]
AI agent vs LLM explained: an LLM generates text, while an AI agent wraps an LLM with tools, memory and a control loop to take actions and complete multi-step tasks.
OWASP Agentic Top 10 explained: the ASI01 to ASI10 taxonomy with real incidents and mitigations for AI agent risks, from a cybersecurity team.
Securing AI agents in production with a six-layer defense architecture, grounded in the March 2026 LiteLLM supply-chain attack and Gartner incident forecasts.
Agentic commerce is AI agents discovering, negotiating and paying for goods and services autonomously. This guide compares the major 2030 forecasts, walks through the machine-to-machine payment flow step by step and explains why on-chain rails fit agent transactions.
The AI agent framework landscape in 2026 has matured significantly from the early days of LangChain-or-nothing decisions. Today, developers choose from at least six production-viable frameworks, each with distinct strengths and tradeoffs. The right framework choice saves months of development time and prevents costly rewrites. The wrong choice locks you into patterns that fight your […]
A spec-current guide to MCP server development after the 2026-07-28 revision, covering the stateless protocol change, the modern-vs-legacy split, server primitives, transports, authorization and the named security failure modes, plus what a server actually costs to build and maintain.
What Article 50 of the EU AI Act actually requires for marking AI-generated content, the two mandatory layers, the transitional date hidden in Article 111(4) and the failure modes the Code of Practice admits marking cannot survive.
Quick Comparison: LangChain vs CrewAI vs AutoGen Factor LangChain/LangGraph CrewAI AutoGen Best for Production systems, complex state Role-based teams, rapid prototyping Code generation, research tasks Learning curve Steep (large API surface) Moderate (role-based abstraction) Moderate (conversation patterns) Production readiness High (LangSmith observability) Medium (growing ecosystem) Low (research-oriented) Multi-agent LangGraph for orchestration Built-in role assignment Conversation-based […]
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Build an agent when the task requires multi-step reasoning, tool use (databases, APIs, code execution), conversation state across turns or routing between specialized capabilities. Use a direct LLM call (OpenAI/Anthropic SDK) for single-shot tasks: classification, summarization, structured extraction, single-question Q&A. Most "AI features" are actually direct calls. Agents are for workflows, not features.
Allow 2-3 weeks for discovery and evaluation planning, then an estimated 6-10 weeks for a single-purpose MVP. A production multi-agent system with monitoring is estimated at 4-6 months.
These are scope-dependent planning ranges, not additive fixed phases or delivery guarantees. Data access, tool integration and acceptance criteria affect the schedule.
Pilot agent $60,000-$120,000. Production multi-agent system $240,000-$680,000. Enterprise platform $510,000-$1,600,000. Cost drivers: number of tools the agent integrates with, evaluation set complexity, observability and audit logging, regulatory requirements, post-launch monitoring tier. The biggest hidden cost is NOT the LLM bill - it is the evaluation set, guardrails and observability you need to safely run agents in production.
Layered controls: grounded retrieval (RAG with citation tracking), structured output schemas with validation, confidence thresholds with human handoff, evaluation set tested on every deploy, runtime guardrails that flag low-confidence answers. We instrument every response so you can audit any answer back to its source documents. Hallucinations cannot be eliminated, but they can be detected, contained and recovered from.
LangChain or LlamaIndex for most production work - biggest ecosystems, best tool integrations. DSPy when we need structured prompt optimization. OpenAI Assistants API or Anthropic Claude tool use for simpler agent patterns. Sometimes no framework at all - a tight loop of LLM call + tool dispatch is often the cleanest production code. The choice depends on agent complexity, observability needs and team familiarity.
Every agent ships with an evaluation set of 150-300 questions tied to business outcomes (deflection rate, citation precision, task completion, customer satisfaction). The eval set runs on every deploy and on a nightly schedule against the production model.
Drift is measured month-over-month on the same eval set with the same model - if accuracy drops more than 3 points, we investigate. Human spot-checks supplement automated evals on consequential decisions.
First establish the data categories, jurisdictions and responsibilities. For a HIPAA-covered workflow, agree the business associate contract where required and the safeguards for PHI.
Model hosting, redaction, access controls and trace retention follow that scope. A BAA is a contract, not an audit certificate. Independent assurance reports such as SOC 2 are a separate engagement and do not certify every system or use case.
EU data subject workflows run inside the region by default (AWS Frankfurt, GCP Belgium, Azure West Europe). Personal data is redacted or tokenized before the LLM ever sees it, with the redaction key held by the client. Every agent decision that affects a natural person is logged with the full prompt, tool calls, retrieved context and final output, so we can satisfy a GDPR right-to-explanation request in minutes rather than days. For EU AI Act high-risk use cases we classify the system upfront against Annex III, document the risk profile, run bias and fairness checks on the evaluation set and maintain a version history of prompts and models so the regulator audit trail is available from day one.
We decline single-prompt features dressed up as agents (use a direct call), agents without an evaluation set tied to business outcomes (no way to know if the agent works), workflows that need zero-error guarantees on individual actions (medical dosing, financial settlement), real-time systems with sub-100ms latency budgets and projects with no plan for prompt maintenance or drift monitoring.
Bring one workflow, the available tools, permitted actions and examples of successful and failed tasks. These inputs let us compare a deterministic workflow, a direct model call and an agent against the same outcome.
Book an AI agent readiness call
Book a 30-minute call with our AI delivery team and walk away with a scoped evaluation set, a sandbox plan and an honest yes-or-no on whether an agent is the right answer for your workflow.
Contact us today to discuss your project. We're ready to review your request promptly and guide you on the best next steps for collaboration
Same dayWe're committed to keeping your information confidential, so we'll sign a Non-Disclosure Agreement
1 dayAfter we chat about your goals and needs, we'll craft a comprehensive proposal detailing the project scope, team, timeline and budget
3-5 daysLet's connect on Google Meet to go through the proposal and confirm all the details together!
1-2 daysAs soon as the contract is signed, our dedicated team will jump into action on your project!
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