01 · Strategy
Use-case identification and ROI modeling.
Where AI moves the needle in your business and where it does not. Honest assessment of feasibility, build-vs-buy, and total cost of ownership before committing.
Service · AI integration
AI consulting and implementation.
Predictive analytics, intelligent chatbots, recommendation engines, and AI integrations across custom and hybrid mobile and web apps. Knowledge-base-grounded assistants engineered to work in production. We use AI tools where they help. Engineers always lead.
Our expertise
AI strategy through production deployment.
We turn AI from demo to production. Strategy in discovery, knowledge-base preparation, model selection, integration with your existing stack, and cost-monitored production deployments.
02 · Chatbots
Knowledge-base-grounded assistants.
Customer support, sales pre-qualification, internal helpdesk. Production-grade with cost monitoring, escalation paths, and accuracy tracking, not demo-quality.
03 · Predictive analytics
Forecasting and anomaly detection.
Demand forecasting, churn prediction, anomaly detection in operations. Models tuned to your data, not generic off-the-shelf scoring.
04 · Recommendation engines
Product, content, and next-best-action.
E-commerce product recommendations, content discovery, personalization. Both collaborative-filtering and LLM-based approaches depending on data and budget.
Capabilities
From data to deployment, one team.
The same senior staff that scopes the engagement is the staff that builds the model, integrates it, and supports it. AI engineering paired with software engineering, not separated.
Model selection & integration
OpenAI, Anthropic Claude, Llama, custom models.
Closed-source for production-quality. Open-source when cost or compliance demands it. Hybrid stacks where each model handles what it is best at.
Knowledge-base preparation
Vector stores, document chunking, retrieval pipelines.
Half the chatbot quality is the knowledge base. We prepare, structure, and maintain the source content alongside the model integration.
Cost monitoring & guardrails
Token-cost tracking, fallbacks, abuse prevention.
Production AI is expensive when uncontrolled. We instrument cost-per-query, set hard ceilings, and degrade gracefully when limits hit.
Compliance & data governance
PII scrubbing, regional data routing, audit logging.
HIPAA-aware health AI deployments. SOC 2 compatible logging. EU data routing where GDPR applies.
How we work
Four phases. Same team across all four.
The phases that apply to every engagement, not just ai consulting. The team that scopes does the building, and the operating.
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Phase 01 · 2–4 weeks
Discovery and scope.
Stakeholder interviews, technical review of existing systems, risk register, written scope with milestones and exit criteria.
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Phase 02 · 3–12 months
Build and iterate.
Two-week sprints with working demos. Senior leads on every sprint review. Code reviewed, accessibility checked.
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Phase 03 · 2–6 weeks
Cutover and stabilization.
Parallel run with rollback path. On-call coverage during the launch window. Stabilization continues until incident rate trends to zero.
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Phase 04 · ongoing
Operate and evolve.
Multi-year retainer with the same team that built the product. Monthly check-ins, quarterly business reviews.
Read the full engagement model on the How We Work page.
Industries we serve
AI Consulting across 15 verticals.
Six core verticals where OST has the deepest engagement experience. Plus nine adjacent industries served on selective engagements.
01
Education
K-12 charter networks, higher education, public sector portals.
02
Nonprofit & Civic
Donor-cycle nonprofits, advocacy organizations, civic platforms.
03
Healthcare & Wellness
HIPAA-aware platforms, medical directories, telemedicine adjacency.
04
Real Estate Technology
Multi-tenant SaaS, brokerage tools, self-storage operators.
05
E-commerce & Marketplaces
OpenCart specialists, custom commerce, $10B+ in transactions processed.
06
Manufacturing & B2B
Industrial platforms, B2B safety-tech, embedded engineering teams.
Also serves on selective engagements
Frequently asked questions
Common questions on ai consulting engagements.
How do you decide whether AI fits a use case?
During discovery we look for repetitive decisions, high-volume queries, or pattern recognition tasks. AI fits when the task has clear inputs and ambiguous outputs that humans currently handle. We say no when a deterministic system would do the job better.
What does a typical AI engagement cost?
Pilot deployments (one channel, basic KB) start around $25K. Production deployments (multi-channel, custom training, escalation flows) run $50K to $150K. Enterprise multi-tenant rollouts run higher. See our AI chatbot ROI calculator for a defensible bracket.
Open-source models or closed-source?
Both. Closed-source (OpenAI, Anthropic, Google) when production quality and cost-per-query are predictable. Open-source (Llama, Mistral) when data sovereignty, fine-tuning depth, or per-query cost dominates the equation.
How do you handle AI cost monitoring?
Token-cost tracking per query, hard ceilings, fallback to cheaper models when traffic spikes, and graceful degradation when limits hit. Cost should be predictable, not surprising.
What about hallucinations and accuracy?
Knowledge-base grounding constrains the model to your documented content. Refusal paths for out-of-scope questions. Continuous evaluation with synthetic test sets and real user feedback.
Ready to build?
Pick a path forward.
Multiple ways to start: schedule a discovery call, run our cost calculator for a budget bracket, or use the contact form for a written response.