Smaitic Labs delivers production-grade AI solutions and custom software engineering for digital commerce, marketplaces, and SaaS platforms. Core solution patterns include Product Recommendations (vector search & re-rankers), Dynamic Pricing & Promotions (elasticity models and policy guardrails), LLM Search & Customer Support (RAG systems with evaluation benchmarks), In-app AI Agents (task automation & tool calling), Predictive Analytics & Real-Time Alerts, and Churn & LTV Modeling. Solutions follow a structured delivery model: Quick Win in 4–6 weeks and production pilot rollout in 8–12 weeks.
Smaitic Labs delivers production AI solutions including product recommendation engines, dynamic pricing systems, LLM-powered RAG search and customer support, in-app conversational AI agents with tool calling, predictive risk alerts, and churn/LTV models.
We engineer software from architecture design to production using a phased delivery model: a Quick Win prototype or simulation in 4–6 weeks, followed by a live production pilot rollout in 8–12 weeks.
Solutions are purpose-built for e-commerce, digital retail marketplaces, B2B/B2C SaaS applications, and modern high-growth tech platforms.
Our engineering stack includes Python/FastAPI, TypeScript/Next.js, vector databases (Pinecone, Weaviate, pgvector), data engineering platforms (Snowflake, BigQuery, dbt, Airflow), and foundational models from OpenAI, Anthropic, and open-source LLMs.
Engineering Solutions
We design AI, software, data, and cloud solutions around your business challenges, existing systems, technical constraints, and operational requirements. The solutions below demonstrate how we solve complex problems across digital commerce, marketplaces, SaaS, and enterprise technology.
Lift CTR, add-to-cart, AOV & conversion.
Product data, events, orders, user/session profiles.
Recommendation engine, real-time API, fallback rules.
CTR lift, add-to-cart rate, conversion rate, AOV.
Quick Win 4–6 w · Pilot 8–12 w.
Embeddings, re-ranker, FastAPI, Airflow/dbt, MLflow.
Cold-start → popularity/backfill. Catalog quality → enrichment checks.
Increase revenue & margin with safeguards.
Orders, inventory, demand, seasonality, competitor feed.
Elasticity model, pricing policy engine, simulation & audit log.
Margin expansion, revenue growth, price-change acceptance.
Quick Win 4–6 w · Pilot 8–12 w.
Snowflake/BigQuery, dbt/Airflow, FastAPI, MLflow, Grafana.
Cold-start → popularity/backfill. Margin safeguards → hard price floors.
Reduce tickets & time-to-answer. Raise CSAT.
Knowledge base, docs, policies, solved tickets, release notes.
Retriever + LLM, citations, guardrails, evaluation dashboard.
Deflection rate, first-response time, CSAT, precision@k.
Quick Win 4–6 w · Pilot 8–12 w.
LangChain/LlamaIndex, vector DB, OpenAI/Anthropic/Llama, FastAPI.
Hallucinations → grounding. Safety → filters. Ops → canary/rollback.
Improve activation and task completion.
Docs/KB, product telemetry, ticket data; optional tool APIs.
In-app assistant component, tool calling (search, create, update), analytics, feature flags.
Activation rate, task completion time, deflection, CSAT.
Quick Win 4–6 w (limited surfaces) · Pilot 8–12 w (core flows).
LangChain/LlamaIndex, OpenAI/Anthropic, FastAPI/GraphQL.
Hallucinations → schema validation & guardrails. Latency → prompt caching.
Surface risks and opportunities in near-real-time.
Product events, billing, CRM/support signals.
Scoring service, thresholds/alert rules, action feeds, dashboards.
Precision/recall vs baseline, alert resolution time, lift in targeted actions.
Quick Win 4–6 w (backtest + dashboard) · Pilot 8–12 w (live scoring + alerts).
scikit-learn/XGBoost, Airflow/dbt, Kafka/Kinesis, MLflow, Grafana.
Alert fatigue → adaptive thresholds & quiet hours. Drift → continuous monitoring.
Reduce logo churn, improve NRR.
Usage cohorts, billing, support interactions, entitlements.
Risk scores, next-best-action rules, cohort dashboards, experiment plan.
Churn rate, retention, NRR, save rate on targeted accounts.
Quick Win 4–6 w (historical score + actions) · Pilot 8–12 w (live scoring + outreach hooks).
LightGBM/XGBoost, Snowflake/BigQuery, dbt, MLflow, Airflow.
False positives → calibrated interventions. Leakage → time-split validation.
Every Solution Covers
Each solution starts with the problem, not a predetermined technology stack. We evaluate the business objective, existing systems, technical constraints, data, integrations, scale, security, and operational requirements before defining the engineering approach.
Define the real business or engineering problem, including key constraints, dependencies, and inefficiencies.
Identify the workflows, operating environments, product scenarios, and technology conditions where the solution can create the greatest practical value.
Design around workload, scale, performance, security, data, and operational requirements.
Define the AI, software, data, automation, and platform capabilities required to solve the problem.
Select the right technologies and integrate with your existing applications, data, APIs, cloud, and systems.
Define success across efficiency, revenue, customer experience, performance, scalability, reliability, cost, and technical risk.
Bring us the challenge, existing systems, and technical constraints. We define the right architecture and bring together the AI, software, data, cloud, security, and production expertise required to solve it.
