The work crosses systems
The request starts in email, the facts live in a data platform, and the final action belongs in an ERP, CRM, case tool, or custom application.
Agentic AI consulting · federal + enterprise
AI systems that do the work—not just talk about it.
OptiSyn helps teams turn slow, manual workflows into secure AI products and agentic operations. We map the work, connect the systems, ship production software, and stay for the hard part.
Senior delivery team · SDVOSB · McLean, Virginia
90%Touchless processing reached
$80MRevenue tied to an AI product
$35MInitiative supported by contract intelligence
800K+Records connected for federal analysis
01 / THE REAL WORK
AI has to survive the day job.
A good demo answers a question. A production system has to find the right evidence, use the right tool, follow the rules, and know when to stop. That is the work we take on.
The answer depends on context
Policy, permissions, account history, contracts, and prior decisions matter more than a polished response from a model.
A bad action has a cost
Money, mission, compliance, or customer trust is on the line, so confidence scores and human approval cannot be afterthoughts.
Someone owns Monday morning
After launch, the system still needs monitoring, support, controlled updates, and a team accountable for the business result.
Human authority stays visibleWorkflow first. Model second.
Example delivery briefContract exception review
PRODUCTION PATH / 06–08 WEEKSObjective
Cut review time without letting the agent approve a financial action or hide the evidence behind its recommendation.
- 01Gather
Pull the contract, account history, policy, and live system data.
- 02Compare
Check the evidence against business rules and prior decisions.
- 03Escalate
Send low-confidence or high-impact decisions to the right person.
- 04Record
Write the approved action back and keep the evidence attached.
- Release test
- Fixed evaluation set
- Human gate
- Financial action
- Proof
- Source + decision trace
02 / AFTER LAUNCH
This is what an agent looks like after the demo.
A production agent should not disappear into a chat window. The work, evidence, confidence, cost, approval path, and final action should be visible to the people responsible for the result.
Agent ControlContract operations
OperationsEvaluationsCost
ProductionLast 30 days
Evaluation performance
First-pass quality
96.8%2.7 pts over baseline
Daily case volumeAverage 43 cases per business day
Median resolution28 secp95 52 sec
Cost per resolved case$0.42Target $0.60
Review SLA94.6%265 of 280 on time
Current run
TRACE 7F3A-29C1Decision trace
- Intake agent
Normalized request and matched account 10492
- Policy agent
Found three applicable pricing controls
- Evidence agent
Compared contract, ERP order, and account history
- Auditor agent
Flagged the financial approval threshold
- Workflow
Assigned decision to A. Morgan
Operations queue
HealthyRecent cases
| Case | Workflow | State | Elapsed |
|---|---|---|---|
| AG-48291 | Contract pricing | Awaiting approval | 00:38 |
| AG-48290 | Provider enrollment | Completed | 00:27 |
| AG-48289 | Policy response | Verifying | 00:14 |
| AG-48288 | Invoice exception | Policy blocked | 00:08 |
03 / WHAT WE DO
Start with the bottleneck. We will bring the right stack.
01
Agentic workflow modernization
Automate the handoffs, not just the answer.
A common starting pointContract review and obligation tracking02
AI product engineering
Turn the use case into a product people will use.
A common starting pointKnowledge and research assistants03
Enterprise knowledge systems
Make scattered knowledge usable without losing the source.
A common starting pointPolicy and program knowledge04
AI-ready data platforms
Fix the data path that is holding the AI work back.
A common starting pointAI and analytics foundations05
Decision intelligence
Give teams a better next move, not another chart.
A common starting pointDemand and inventory planning06
Responsible AI and assurance
Test it before employees or customers depend on it.
A common starting pointProduction-readiness assessment07 / RUN
Managed Agent Operations
We monitor production behavior, handle incidents, tune quality and cost, and keep the workflow aligned as business rules change.
04 / SELECTED WORK
The kind of work we have actually delivered.
Representative delivery experienceU.S. Army
AI agents and operational data met inside Palantir.
Mission workflows needed governed access to multiple data and media sources without creating another disconnected AI interface.
AIP + FoundryProduction orchestration
ResultAgents, data, and operational context ran inside the platform the mission already used, creating a reusable foundation for additional workflows.
See the delivery story02 / Fortune 10 healthcare
A commercial AI product scaled to 90% touchless processing.
The product line reached 90% touchless processing while preserving a controlled path for exceptions and human review.
03 / Enterprise healthcareContract agents turned restrictive terms into actionable findings.
The workflow supported a $35M operating initiative with findings teams could validate, prioritize, and act on.
04 / U.S. Department of StateGoverned budget knowledge moved from search to decision support.
Research that took hours could be completed in seconds while keeping evidence and permissions attached to the answer.
Outcomes reflect programs led or delivered by OptiSyn leadership and teams. Client ownership, attribution, and confidentiality vary by engagement.
05 / WHO WE HELP
Different industries. The same need for control.
FED / DEF01
Federal civilian and defense
Mission AI that holds up under real oversight.
Program offices, mission owners, CIO organizations, and delivery primesHC / LS02Healthcare and life sciences
AI for the work where revenue and compliance meet.
Operations, product, compliance, finance, and data leadershipENT / OPS03Regulated and knowledge-heavy enterprise
Fix the work that falls between systems.
COOs, CIOs, operations, finance, procurement, and service leadersPRIME / TEAM04Integrators and government contractors
Senior, cleared AI capacity that strengthens the team.
Capture leaders, program executives, solution architects, and subcontract managers06 / HOW TO START
A clear first step, not a six-month discovery phase.
Bring us a workflow and an owner. We will help size the value, test whether AI is the right fit, and recommend the smallest production path that can prove the case.
01 / DEFINE
Agentic Opportunity Blueprint
Typically 2 weeksChoose the right workflow, size the business case, map the risk, and leave with a plan an engineering team can actually build.
02 / PROVEProduction Agent Sprint
Typically 6–8 weeksPut one valuable workflow into users’ hands with real integrations, representative test cases, clear controls, and a measurable release decision.
03 / SCALEAgentic Modernization Pod
90-day incrementsA senior cross-functional team works through a connected set of workflows, data products, and controls tied to one operating result.
04 / OPERATEManaged Agent Operations
OngoingWe run, monitor, and improve agent-driven workflows with defined service levels, human escalation, and outcome reporting.
07 / WHY OPTISYN
Senior people stay close to the work.
OptiSyn is deliberately compact. The people who shape the solution stay involved in the architecture, build, testing, and production handoff. You do not buy a senior pitch and receive a junior team.
About the firmWorking software, not a strategy binder
Every engagement ends with a tested capability, a buildable plan, or an honest no-build decision.
Acceptance criteria before launch
Representative data, failure thresholds, and approval boundaries make “ready” a shared decision.
Built inside your environment
We work with the cloud, data, identity, and operating systems your team already owns.
A team that can leave cleanly
Source, runbooks, telemetry, architecture, and decision rights remain usable after the engagement.
08 / BUYER QUESTIONS
What teams ask before they fund the work.
What does agentic AI consulting actually include?
It starts with the workflow, not the model. We map the decisions and handoffs, connect the right data and systems, build the agent or application, define human approval points, test it on representative cases, and put monitoring around the production result.
How long does it take to build a first production AI agent?
A focused workflow can often reach a controlled production release in six to eight weeks. More complex programs usually start with a two-week blueprint to confirm the business case, data access, integrations, risk, and acceptance standard before engineering begins.
Do we need perfect data before we start?
No. We need enough representative data to understand the work, define a release test, and see the highest-risk gaps. We will separate what must be fixed now from what can be handled through controls or moved into a longer data plan.
Can you work inside our existing cloud and tools?
Yes. We work across Azure, AWS, Palantir, Databricks, Microsoft Fabric, modern data stacks, and custom enterprise applications. We design around the environment your team already operates instead of forcing the project onto a preferred vendor stack.
How do you keep agents from taking the wrong action?
We limit the tools an agent can use, define permission boundaries and stop conditions, require human approval for high-impact actions, and keep a rollback path. Release is tied to a fixed evaluation set, and the same checks continue after launch.
Do you support government contracting and prime teams?
Yes. OptiSyn Solutions is an SDVOSB with CAGE 9Q8U6 and UEI DQV6LH2XHDM4. We can lead focused workstreams or add senior AI, data, Palantir, and analytics capacity to a larger team.
Start with the workflow
Tell us what is slow, expensive, or stuck.
We will tell you whether an agent belongs in it, what the first production step should be, and where the real risk sits. You will hear back within one business day.
- Business
- SDVOSB certified
- Delivery
- Cleared + regulated
- Location
- McLean, Virginia
Project briefSecure intake · unclassified