Viacheslav Kostin
CEO WislaCode Solution
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AI and ML Development Services
At WislaCode, we specialise in delivering cutting‑edge AI and machine learning development services, enabling organisations to harness the full potential of artificial intelligence. Our focus is custom engineering and implementation within your environment – so you gain practical, measurable impact without a boxed product. Every engagement is shaped around your data, controls and workflows.
AI development overview
AI development spans methods that enable systems to learn, adapt and support decisions with human‑grade judgement.
We provide end‑to‑end services – from discovery and design to deployment and ongoing support. Keeping solutions effective, scalable and aligned with governance, security and cost targets.
Strategic AI consulting
We align initiatives with measurable outcomes. Work includes opportunity discovery, data readiness, risk assessment and a pragmatic delivery roadmap. Guidance covers model selection (LLMs, classical ML, CV, NLP), build‑versus‑buy decisions, MLOps foundations and governance that scales with your organisation.
Seamless deployment strategies
Deliver cloud, on‑premise and hybrid rollouts with CI/CD, model registries and environment parity. Blue‑green and canary releases reduce risk, while observability covers latency, throughput, cost and accuracy. Data pipelines, feature stores and API‑first integration connect models to real‑world workflows.
AI solutions development
We build production‑grade applications – intelligent search, recommendation engines, chatbots and agents, computer vision and predictive analytics. Safeguards, audit trails and monitoring come by default, with clear ownership models for ongoing iteration and value realisation.
Human-AI interaction design
We craft experiences where people and models collaborate effectively. From prompt/response patterns to conversational flows, guardrails and explainability, we optimise for usability, trust and adoption. Accessibility, localisation and role‑based views ensure the right insight reaches the right person at the right moment.
Algorithmic expertise
From gradient‑boosted trees and time‑series forecasting to transformers, RAG and multimodal models, we choose approaches that fit your data, constraints and SLAs. We handle feature engineering, hyperparameter tuning, evaluation design, bias checks and performance optimisation for reliable, reproducible results.
Scalability and performance optimisation
Optimise inference with batching, quantisation and distillation, and scale elastically across services. Capacity planning, caching, vector storage and workload isolation keep experiences responsive while controlling cost. Continuous testing and drift detection sustain quality at scale.
Directions in AI and ML development
Custom assistants for customer support and internal service, tuned to your tone, policies and knowledge – learning from interactions under controlled feedback loops.
Task‑focused agents that orchestrate workflows and tools within guardrails. We co‑design policies, escalation paths and observability to keep autonomy safe and auditable.
Retrieval‑augmented search that indexes your data sources and returns precise answers with citations. Designed for relevance, freshness and data‑access controls.
Forecasting and propensity models using historical signals to guide planning, pricing and retention – evaluated against business KPIs, not just technical metrics.
Computer‑vision pipelines for detection, classification and quality checks – with data governance, annotation strategy and model lifecycle management.
Document understanding, classification, extraction and summarisation across multi‑format corpora – delivered with redaction and privacy‑aware processing.
Personalised suggestions driven by behaviour and context, with explainability and bias monitoring to support trust and compliance.
How we deliver?
We assess requirements, select suitable technologies and define a clear, incremental plan tied to success metrics.
Analysis
Planning
Integrate AI with your systems via APIs, SDKs and event streams – ensuring interoperability, observability and secure data flows.
Integration
Design and implement models tailored to your use cases, with evaluation protocols that reflect real business outcomes.
Model Development
Run rigorous testing for performance, reliability, safety and security, with acceptance criteria agreed upfront and met before release.
Quality Assurance
Case: On‑premise AI search cuts data retrieval time by 70%
A client needed accurate, secure search across fragmented knowledge without relying on third‑party services. In six weeks, we delivered an AI‑powered assistant that indexes internal sources and returns precise answers instantly.
Built entirely on‑premise, it integrates with existing systems and supports open‑source and private models (including LlamaIndex and Hugging Face components). Strict data controls ensure full compliance.
Outcomes included about 70% faster retrieval. The architecture is scalable and keeps improving search accuracy. This was implemented in the client’s infrastructure, not as a ready-made product.
Why WislaCode?
Start with the metrics that matter – time saved, conversion uplift, risk reduction and design increments that prove value quickly. Clear acceptance criteria and regular demos keep teams aligned.
Outcome‑driven AI delivery
Least‑privilege access, encryption in transit and at rest, data minimisation and retention policies are built in. Consent workflows, redaction and human‑in‑the‑loop reviews protect stakeholders and uphold trust.
Security
Privacy
Reproducible pipelines, model registries, evaluation suites and monitoring are standard. Versioned datasets, audit trails and rollback plans let your teams operate with confidence.
Engineering rigour
MLOps by default
API‑first patterns, SDKs and event streaming connect models to apps, data stores and analytics. We design for interoperability, future model swaps and low‑friction maintenance.
Seamless integration
Some Customer Reviews
WislaCode specialists and AI developers are dedicated to each project. They work hard to deliver results quickly.
Julia Dvornikova, Taal Healthtech
5
We collaborated with WislaCode on a route-to-market optimisation project.
Working with WislaCode was effective, transparent and predictable, which is especially critical for AI and ML projects.
Read the full review
FAQ About AI and ML Development Services
We begin with problem framing and constraints: data volume and quality, latency and cost targets, privacy requirements and integration needs. Where context is diverse and language‑heavy, we lean on foundation models with retrieval augmentation. For structured predictions or limited data, classical ML often wins on efficiency. Bespoke training is reserved for gaps unserved by off‑the‑shelf options. We validate the shortlist through small experiments with clear success criteria before committing.
Yes. We design air‑gapped or controlled‑egress architectures using self‑hosted components for vector search, orchestration and inference. Containerised services and infrastructure‑as‑code ensure reproducibility across environments. Model registries, access controls and audit logging maintain governance, while hardware acceleration is calibrated to workload needs. The outcome is full data control, predictable performance and compliance with internal policies.
Reliability comes from layered safeguards: evaluation suites, guardrails, schema validation and fail‑safe behaviours. We monitor latency, cost, accuracy and drift, with alerts tied to agreed thresholds. Rollouts use canary or feature‑flagged paths, and fallbacks route requests to known‑good versions. We use content filters, red-team tests, and role-scoped permissions for safety. Also, we have human-in-the-loop escalation for sensitive actions.
It covers the full lifecycle: data quality checks, feature stores, experiment tracking, model versioning, CI/CD for training and inference, and comprehensive observability. We define evaluation protocols that reflect business impact, not just technical metrics. Automated retraining pipelines manage drift, while governance records lineage and approvals. Documentation and playbooks empower teams to iterate safely without vendor lock‑in.
We target a first value slice in four to six weeks: a working pathway from data to a limited‑scope capability, instrumented for measurement. Later increments expand coverage, refine relevance and improve latency or cost. This approach reduces risk, builds stakeholder confidence and ensures investment tracks tangible outcomes rather than assumptions.
We start with clear definitions of harm and fairness relevant to your context, then design datasets and evaluations accordingly. Techniques include stratified testing, counterfactual checks and model cards. Where explanations matter, we provide local or global interpretability and rationale summaries. Governance artifacts document intent, limitations and approved usage to support responsible deployment.
We favour API‑first integration with well‑defined contracts, event streaming for asynchronous workflows and adapters for line‑of‑business systems. Data access uses the principle of least privilege. It includes row or field-level controls when needed. Telemetry feeds analytics, enabling informed iteration and transparent reporting to stakeholders.
Yes. We design modular runtimes that allow model swaps based on task, cost and performance. Retrieval and orchestration layers are model‑agnostic, so you can adopt open‑source for control and cost, or commercial models where quality or features demand it. This flexibility reduces lock‑in and future‑proofs your platform.
We right‑size infrastructure, apply batching, caching and quantisation, and choose efficient architectures for the task. Autoscaling policies balance responsiveness and spend. Continuous load testing and SLOs keep systems within agreed bounds, while dashboards make trade‑offs visible to product and finance stakeholders.
We provide documentation, training and shadowing across pipelines, deployment and monitoring. Runbooks and dashboards support day‑to‑day operations, while coding standards and templates accelerate future work. The goal is autonomy: your teams can extend and maintain solutions confidently after go‑live.
Viacheslav Kostin, CEO
20+ years of experience in managerial positions in IT and banking.
Viacheslav Kostin, CEO
Previous roles: CEO in IT, Director of Strategy and Marketing in Banking, Curator of Holding Banks, Head of Products and Project Office.
Education: MBA for Executives at IMD (Switzerland), Leading Digital Business Transformation (IMD). Provides consulting in strategy and digital transformation.
Vasil Pahomov, CTO
20+ years of experience as a developer, analyst, and solutions architect.
Vasil Pahomov, CTO
Designs resilient, high-load systems with multiple integrations for banks and financial institutions. Expertise in distributed storage and microservices architecture.