AI spending often grows quietly as systems become more complex. Large prompts, repeated context, unnecessary tool calls, inefficient retrieval, excessive retries, and uncontrolled agent loops can all increase cost without improving the final outcome.
OST evaluates how your AI assistants, RAG applications, and multi-agent systems actually operate in production. We identify where tokens, compute, API calls, infrastructure, and agent steps are being wasted, then implement practical improvements across model selection, prompts, retrieval, caching, routing, orchestration, and workflow controls. The goal is not simply to use cheaper models or fewer tokens. It is to achieve the lowest sustainable cost per successful business task while maintaining agreed standards for quality, speed, reliability, and user experience.
Our expertise
Provider invoices show total usage, but not always which customer, feature, workflow, or agent created it. We instrument model calls, tokens, tool use, retries, latency, failures, and cost by task—creating the baseline needed to prioritize meaningful savings.
What we optimize.
Routing
Routine requests should not automatically go to the most expensive model. We classify tasks, benchmark suitable models, and route each request to the lowest-cost option that meets agreed quality and latency requirements. More capable models remain available for complex work and fallback scenarios.
Efficiency
Agents often resend large instructions, conversation histories, documents, and tool definitions on every step. We remove redundant content, improve retrieval, limit context to relevant information, set appropriate output lengths, and reduce calls that do not add value.
Caching
Repeated instructions, knowledge, or responses should not always be processed from the beginning. We implement prompt, response, or semantic caching where the workload and provider support it, then monitor cache-hit rates and actual savings.
Processing
Not every task needs an immediate response. Data enrichment, document processing, classification, evaluation, and other non-urgent workloads can often use batch or lower-priority processing to reduce cost while preserving user-facing performance.
Workflow
Multi-step agents can multiply usage through planning cycles, tool calls, handoffs, and retries. We simplify orchestration, limit loop depth, validate tool output, stop duplicate work, set retry ceilings, and require human approval before costly or consequential actions when appropriate.
Infrastructure
We review timeouts, fallbacks, rate limits, vector search, API gateways, provider plans, and infrastructure. The goal is a balanced system—not a lower model bill that increases cloud cost or weakens reliability elsewhere.
The integration requirement
Every optimization is evaluated against representative business tasks. We compare accuracy, task completion, groundedness, latency, failure rate, and cost before and after each material change. If a cheaper configuration cannot meet the acceptance threshold, it is not released
Depending on scope, deliverables may include:
How we work
The phases that apply to every engagement, not just agentic ai optimization. The team that scopes does the building, and the operating.
Phase 01 · 2–4 weeks
Stakeholder interviews, technical review of existing systems, risk register, written scope with milestones and exit criteria.
Phase 02 · 3–12 months
Two-week sprints with working demos. Senior leads on every sprint review. Code reviewed, accessibility checked.
Phase 03 · 2–6 weeks
Parallel run with rollback path. On-call coverage during the launch window. Stabilization continues until incident rate trends to zero.
Phase 04 · ongoing
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.
Share one month of provider billing, your architecture, and one high-volume workflow. OST will identify what is driving the spend, where optimization is practical, and which changes are likely to produce the strongest return.
Industries we serve
Six core verticals where OST has the deepest engagement experience. Plus nine adjacent industries served on selective engagements.
01
K-12 charter networks, higher education, public sector portals.
02
Donor-cycle nonprofits, advocacy organizations, civic platforms.
03
HIPAA-aware platforms, medical directories, telemedicine adjacency.
04
Multi-tenant SaaS, brokerage tools, self-storage operators.
05
OpenCart specialists, custom commerce, $10B+ in transactions processed.
06
Industrial platforms, B2B safety-tech, embedded engineering teams.
Also serves on selective engagements
Frequently asked questions
Yes. We can review a production system, prototype, or third-party integration using its architecture, usage, billing, and performance data.
It should not. We establish acceptance criteria first and test every material change. Savings are evaluated together with quality, speed, and reliability—not in isolation.
Savings depend on request volume, model selection, prompt size, agent complexity, retry behavior, and the controls already in place. OST establishes a baseline, estimates each opportunity, and measures verified savings after implementation. We do not promise a fixed percentage before reviewing your actual usage.
We evaluate the complete operating cost, including tokens, embeddings, vector databases, reranking, agent tools, third-party APIs, retries, cloud infrastructure, storage, monitoring, and support overhead. This prevents a model-level saving from unintentionally increasing costs somewhere else.
We trace each workflow from the initial request through model calls, retrieval, tool usage, agent handoffs, retries, and completion. Costs can then be attributed by feature, customer, department, or task. The most useful measurement is usually cost per successful outcome—not cost per individual model call.
Not necessarily. Many systems can reduce costs through better prompts, context management, caching, routing, and workflow controls while keeping their existing provider. We recommend changing a model or provider only when testing demonstrates a meaningful cost, quality, reliability, or operational advantage.
We can establish limits for agent steps, token usage, tool calls, retries, execution time, and total task cost. Duplicate-action detection, output validation, circuit breakers, fallback rules, and human approval can also stop an agent before it repeats work or performs an unnecessarily expensive action.
Model routing sends routine tasks to efficient models while reserving more capable models for complex, sensitive, or uncertain requests. It works best when requests can be classified reliably and every route has measurable quality requirements, fallback rules, and ongoing performance monitoring.
It depends on the information, provider, and workflow. OST evaluates cache isolation, retention, expiration, invalidation, and platform controls before enabling it. Sensitive, customer-specific, or frequently changing information may need to be excluded or cached within stricter boundaries.
No. Customer conversations may require immediate responses, while document processing, classification, enrichment, reporting, and evaluation can often run asynchronously or in batches. Separating urgent and non-urgent workloads can reduce operating costs without negatively affecting the customer experience.
Ready to build?
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.
No pitch deck. We tell you straight if we are a fit.
Book a time →A defensible budget bracket at our blended rate. No email gate, no upsell.
Use the calculator →A written response from a senior, within one business day.
Contact Us →
