AI Software Development Services
From data to deployed models

Our services cover strategy and discovery, data engineering, model development (ML, GenAI, agentic AI), MLOps, security/governance, and production integration across web, mobile, and enterprise stacks. We start with measurable outcomes—cycle-time reduction, revenue lift, or cost-to-serve savings—and build a phased roadmap from PoC to scale. Explore our approach: AI Development Services

Yes—every engagement anchors on a business case (e.g., claims automation, demand forecasting, sales copilot) with KPIs and guardrails. We validate with a PoC, harden the stack for production, and transfer capability to your team with documentation and MLOps.

Traditional apps encode rules explicitly; AI systems learn patterns from data and continuously improve with feedback. We combine both—deterministic workflows where precision is vital, and learning components where variability is high—to deliver reliable, explainable outcomes.

We separate feature stores, model services, and orchestration layers, and standardize on CI/CD for models with rollbacks. Horizontal scaling, caching, and asynchronous pipelines ensure throughput for spiky workloads without degrading latency.

Choose custom when your workflows, data sources, or compliance needs are unique—or when AI becomes core IP and competitive advantage. Off-the-shelf is fine for commodity tasks; we’ll advise candidly to avoid reinventing the wheel.

Yes—US/EU overlap hours, USD/EUR billing, and enterprise security practices are standard. Our pods include PM/Architects/Engineers/QA/DevOps/MLOps; we align to your SDLC and governance. See also: AI/ML-Led Innovation.

We quantify manual effort saved, conversion uplift, error-rate reductions, or service levels improved. Those metrics shape a milestone plan: Discovery → PoC (2–6 weeks) → MVP (8–12 weeks) → Scale (quarterly increments).

We serve healthcare, ecommerce/retail, logistics/supply chain, manufacturing/Industry 4.0, fintech and professional services. Domain adapters, compliance packs, and prebuilt connectors reduce time-to-value per industry. See: Healthcare Software.

Yes—VPC or on-prem deployments, encryption at rest/in transit, RBAC/ABAC, audit trails, and hardened build pipelines. For healthcare there’s HIPAA; for EU we align with GDPR and optional data residency.

We run short, outcome-focused sprints with weekly demos, KPI dashboards, and risk logs. Executive steering reviews keep budget, scope and value synchronized, reducing change-management friction.

We map pain points to feasibility and value matrices, prioritize 2–3 “needle-mover” candidates, and define success metrics. The outcome is a 90-day plan that balances wins with foundational capability building.

It blends centralized platform (data, tooling, governance) with federated product teams delivering use cases. Clear RACI for data owners, model owners, and risk/compliance keeps velocity high without losing control.

We insist on production-ready design even at PoC: CI/CD for models, observability, and data contracts. If the PoC meets KPI gates, it graduates cleanly into MVP without rework.

Our focus is augmentation: reduce repetitive work, improve decision-support, and free specialists for higher-value tasks. Productivity gains come with role design, training, and safe-use policies—not layoffs by default.

We phase spend by milestones, tie costs to value delivery, and instrument the stack to verify returns. This keeps CFOs confident and avoids “AI as a sunk cost.”

Guardrails, red-teaming, prompt/content policies, PII checks, and approval workflows. A governance board reviews sensitive releases while allowing fast iteration on low-risk features.

Yes—charter, operating model, tooling standards, and enablement tracks for product, engineering, and business teams. We provide playbooks so the CoE becomes a value engine, not a bottleneck.

We score options on time-to-value, TCO, lock-in risk, compliance, and differentiation. Commodity capabilities get integrated; differentiating capabilities get built and owned.

We trace each use case to strategic themes (profitability, growth, risk reduction) and exec KPIs. Roadmaps adapt quarterly to market signals and internal learnings.

Champion networks, role-based training, in-app guidance, and transparent success metrics. Early wins create momentum; feedback loops keep models honest.

Medallion or data mesh with governed schemas, feature stores, and quality SLAs. Stream and batch pipelines coexist, with lineage and contracts for reliability.

Automated profiling, dedupe, entity resolution, and semantic enrichment. We capture quality metrics so model teams know when data is trustworthy.

It’s a governed catalog of reusable ML features with versioning, online/offline parity, and access control. It reduces duplication, improves accuracy, and speeds new model delivery.

Region-scoped storage, residency controls, and policy enforcement at ingestion and access. Contracts and DPA/SCCs formalize cross-border data flows where applicable.

Event streaming, CDC from transactional systems, and low-latency caches for inference. Back-pressure, retries, and DLQs protect reliability during spikes.

Yes—we add semantic layers, feature pipelines, and ML access points without disrupting BI. We build adapters for ERP/CRM/ecommerce and operational databases.

Metadata capture end-to-end, versioned transformations, and link-backs to sources. Auditors can trace metrics to raw data with evidence.

We define thresholds per domain (freshness, completeness, validity) and alert on violations. SLAs drive accountability and stabilize downstream models.

Yes—OCR/NLP pipelines, embeddings for retrieval, and vector stores for semantic search. For media, we use transcoding and content hashing for catalog integrity.

PII scrubbing, policy filters, and retrieval scoped to approved corpora. Prompts and completions are logged with PII masking for audits.

OpenAI/Anthropic/Google/Azure OpenAI and open-source (Llama/Mistral) with LangChain/LlamaIndex orchestration and vector stores (pgvector/FAISS). Selection depends on risk, cost, and deployment constraints.

Retrieval-Augmented Generation (RAG) with citations, curated knowledge bases, and automated evaluations. Humans approve high-risk outputs; logs support review and improvement.

A copilot assists a user inside a workflow; an agent takes autonomous steps toward a goal. We add guardrails and approvals so agents remain safe and traceable.

Both—prompt engineering + tool use for speed; fine-tuning when domain language or tasks are stable and high-value. We document trade-offs and TCO.

Model registries, semantic versioning, A/B tests, and rollback hooks. Offline and online evals ensure quality before full rollout.

Yes—predictive models power pricing, risk, demand, and routing; GenAI explains or actionizes those predictions for users. The combo increases trust and adoption.

We structure system/user prompts, add function-calling/tooling, and include factual context. Prompt templates are versioned and tested like code.

Yes—image captioning/vision tasks, speech-to-text for ops, and multimodal search for knowledge bases. We tune pipelines to cost and latency limits.

Bias assessments, representative datasets, and post-hoc calibration. We record model cards and decisions to support internal and external reviews.

We provide feature importances, traces, and citation trails. For high-stakes use, explainability is a release criterion, not a nice-to-have.

CI/CD for models, feature stores, automated tests, drift detection, canary deploys, and observability dashboards. Incident playbooks and SLOs keep systems reliable.

Statistical monitors on input distributions and output metrics; alerts trigger retraining or fallbacks. We track concept drift and recency effects explicitly.

Yes—Kubernetes, GPU pools, and private endpoints inside your network. We align to your infosec policies and change control.

Caching, batching, adaptive routing (cheap ↔ best), and quantized models. We monitor $/request and $/KPI to avoid runaway bills.

Low-latency model servers, edge caches for context, and asynchronous post-processing. SLIs/ SLOs highlight hotspots per route and user segment.

Blue-green or canary deployments with automatic rollback on threshold breaches. Safe defaults ensure business continuity if a model underperforms.

Yes—with redaction and access controls. Logs fuel QA, governance, and continual improvement without exposing sensitive data.

Offline eval suites, scenario tests, red-team prompts, and pilot cohorts. We gate go-live on factuality, safety, latency, and ROI thresholds.

Yes—Grafana/Prometheus, Datadog, or cloud-native tools. We unify app + model telemetry to see user and system impact together.

Yes—response time, uptime, and accuracy/quality SLOs. We run monthly service reviews and quarterly roadmap updates.

Input sanitization, output filters, allow-listed tools, and isolation of secrets. High-risk actions require explicit human confirmation.

We respect licensing, use your owned data where required, and isolate datasets per client. Fine-tuned weights and code are delivered to your repos.

PII detection/redaction, purpose-limited retrieval, and opt-out pathways. We minimize storage of raw interactions and encrypt everything at rest and transit.

Yes—healthcare projects align to HIPAA safeguards; EU projects follow GDPR with residency options; dev processes map to SOC 2-style controls. See: HIPAA-Secure Platforms.

Yes—RBAC/ABAC, API gateways, and tenant isolation. Policies propagate to prompts and retrieval so users see only what they’re allowed.

Change requests, risk assessment, sign-offs for high-impact updates, and staged rollouts. Everything is auditable with versioned artifacts.

Yes—policy libraries, classifier shields, and post-filters. Unsafe outputs are blocked or redirected with safe alternatives and guidance.

Evidence packs for pipelines, models, access logs, and change management. We generate auditor-friendly documentation as part of delivery.

Yes—prompt injection, jailbreak attempts, data leakage tests, and adversarial content. Findings are triaged and mitigated before scale-up.

Severity classification, on-call rotations, mitigations/rollbacks, and RCA reporting. We add tests and monitors to prevent recurrence.

Predictive maintenance, quality inspection (vision), energy optimization, and dynamic scheduling. We integrate with MES/SCADA and keep latency within line-speed limits.

Demand forecasting, replenishment, lead-time prediction, and anomaly detection in logistics. Dashboards show exceptions and recommended actions with confidence.

Yes—NLP on contracts, risk scoring from signals, and duplicate/overcharge detection. Approvals include explainability for auditability.

Semantic search, recommendations, dynamic pricing/promo optimization, and service copilots. We guard Core Web Vitals while adding relevance. See: Ecommerce Development.

Account insights, proposal copilots, next-best-action, and churn risk alerts. Integrations with CRM/CPQ make workflows faster and smarter.

Deflection via GPT-powered assistants with retrieval from your KB, plus agent assist for faster resolution. We measure CSAT, FCR, and AHT to prove gains.

Yes—PHI-safe pipelines, human review, and explainability; integrations via FHIR/HL7 and SMART-on-FHIR. Details: Healthcare Software.

Close acceleration, anomaly detection in journals, and narrative reporting. Controls and approvals remain intact with better speed and accuracy.

Screening copilots, skills inference, and internal mobility suggestions with fairness checks. HRIS integrations keep records consistent.

Failure prediction, parts recommendation, and visual guidance on mobile. Scheduling agents cut travel time and increase first-time fix rates.

We constrain scope, add citations and “why” explanations, and log decisions for review. Feedback tools let users correct the copilot, improving it over time.

Yes—side-panel assistants, inline suggestions, and context-aware shortcuts. We track usage to ensure the AI is saving time, not adding clicks.

Task completion time, user satisfaction, error rates, and revenue or cost KPIs. We compare cohorts with/without AI to validate impact.

Yes—WCAG-compliant UIs, captioned media, and multilingual prompts/responses. We localize model behavior for region-specific terminology.

Yes—speech-to-text, diarization, and action extraction into CRM/PM tools. Privacy filters prevent sensitive content from leaking.

PoC in 2–6 weeks, MVP in 8–12 weeks, then quarterly scale-up. Data readiness and integration complexity are the biggest timeline drivers.

Fixed-price for well-defined scope; T&M or managed teams for evolving roadmaps. We link payments to milestones and measurable outcomes.

Data cleanup effort, number of integrations, compliance requirements, and latency/availability SLAs. We de-risk unknowns during discovery to keep budgets tight.

Absolutely—choose one high-value workflow, measure uplift, then reinvest. This reduces risk and builds the case for broader adoption.

Yes—role-based training, admin playbooks, and office hours. We aim for your teams to own and extend the solution confidently.

We weigh accuracy, latency, cost, compliance, and IP sensitivity. Many clients start vendor LLMs for speed, then migrate sensitive workloads to tuned open-source.

Yes—connectors and APIs for SAP, Oracle, MS Dynamics, Salesforce, Snowflake/Databricks, etc. We respect rate limits, retries, and data contracts.

React/Next.js, mobile (React Native or native), and desktop. We keep bundles light and avoid blocking main threads with heavy AI logic.

Sandbox environments, contract tests, golden datasets, and synthetic events. We prevent production outages by catching schema and quota issues early.

Yes—with tool/Function Calling gated by policies and approvals. Sensitive operations require multi-step confirmations and full audit trails.

Automated evals, human spot checks, and complaint/error funnels. We trigger retraining or prompt updates when quality drifts.

Load tests, concurrency modeling, and token/latency budgets. We scale horizontally and cache aggressively to meet SLOs.

Yes—autoscaling, back-pressure, and priority queues for critical requests. We plan freeze windows and rollback playbooks for launches.

Fallbacks to cached responses, alternate providers, or reduced-capability modes. Health checks and circuit breakers isolate failures.

Yes—SLA tiers with on-call rotations and response/resolution targets. We provide monthly service reviews and roadmap planning.

Hands-on enablement: prompt patterns, eval writing, and safe-use policies. We mentor product owners and engineers to run AI projects independently.

Outcome metrics (revenue, cost, risk), adoption metrics (active users, tasks assisted), and reliability metrics (latency, accuracy). Dashboards make trade-offs visible.

Central governance for data/models, approved tool lists, and secure sandboxes. Teams can innovate without risking compliance or duplication.

Yes—shared repos, joint rituals, and pairing. We transfer patterns and code so velocity remains after we exit.

Risk registers with likelihood/impact, mitigations, and testing evidence. We tie risks to business controls and compliance obligations.

Pick one process with measurable pain (e.g., claim intake, lead triage, or forecast). In 2–6 weeks we can ship a PoC with metrics and a scale plan.

Balance impact, feasibility, and visibility. We recommend one operational win, one revenue-adjacent win, and one enablement foundation.

Define stakeholders, workflows, data sources, constraints, and success metrics. Even a one-pager accelerates discovery and keeps scope honest.

Start with a discovery call, then a short discovery sprint for clarity on scope, effort, and ROI. From there we propose a PoC/MVP plan with milestones. Contact: AI Development Services.

We deliver production-grade AI, not demos—governed, observable, and tied to business KPIs. Our cross-industry patterns, security posture, and pragmatic MLOps help you realize value fast and scale with confidence. For regulated work, see: HIPAA-Secure Health Tech