As Agentic AI Development Services serving San Francisco, we build agents that close the reconciliation gap directly, working through a workflow and acting inside your real systems instead of stalling at the first case that looks unfamiliar. Every credential and every piece of infrastructure stays under your own control, so none of this depends on us once the build is handed over.
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80+
Workflows Automated
60+
Engineers In-House
96%
Client Retention
Built for teams at a specific inflection point.
From a single scoped agent to a coordinated multi-agent platform, we design, build, and operate across the full spectrum. Start where the value is clearest and expand from there.
Each build starts narrow: one agent, one workflow, with a hard limit on what it can decide by itself and a defined escalation path for everything past that limit.
Single-agentMulti-step reasoningTool use
Each build starts narrow: one agent, one workflow, with a hard limit on what it can decide by itself and a defined escalation path for everything past that limit. The agent gets proven against your own data before it ever touches a live queue.
Some processes are not ready for an agent, and saying so honestly is the whole point of this step.
FeasibilityArchitectureRoadmap
Some processes are not ready for an agent, and saying so honestly is the whole point of this step. Whether agentic AI development services suit a particular workflow gets settled here, before any commitment to build.
Product, finance, and research records rarely live in one system, so a process that spans all three usually needs several agents working in coordination.
Agent orchestrationLangGraphCrewAI
Product, finance, and research records rarely live in one system, so a process that spans all three usually needs several agents working in coordination. This pillar is that coordination layer: handoffs, shared state, and escalation rules that keep the whole thing auditable.
Instructions get read, variation gets handled, failures get retried on their own, and a person only steps in where a genuine decision is required.
Full workflow executionEvent-drivenHuman-in-the-loop
Instructions get read, variation gets handled, failures get retried on their own, and a person only steps in where a genuine decision is required. The agent takes on the process itself, not a simplified version of it.
An agent that cannot reach a system is only useful on paper.
REST & webhookCRMERPLegacy connectors
An agent that cannot reach a system is only useful on paper. This pillar covers connecting it to CRM, ERP, and the specialised platforms running across San Francisco's startups, biotech labs, and financial firms, with the integration mapped out well before any sprint starts.
Answers trace back to real policies, research documents, and internal records, each carrying its own citation.
Vector storesRetrieval pipelinesGrounded outputs
Answers trace back to real policies, research documents, and internal records, each carrying its own citation. That is the line between an answer a team can verify and one they simply have to accept.
What we have built, across categories.
What has actually shipped, sorted by category.
Where speed matters, but so does not breaking things later.
Pull metrics across systems, generate investor updates on schedule, and flag data that does not reconcile before a board meeting. Founders weighing whether to build this in-house or bring in help often look at SME digital transformation on a limited budget first.
Set up accounts, permissions, and integrations for new customers without manual setup work.
Read intent and urgency on inbound tickets, route to the right queue, and resolve routine cases without a handoff.
Where research data meets decisions that used to need a scientist's judgment.
Pull trial data from multiple sources, check it against protocol requirements, and flag discrepancies for review.
Extract findings, methods, and results from papers and lab reports, structuring them for downstream analysis.
Assemble documentation against a checklist, flag missing items, and prepare submission packages for review.
Where accuracy and a defensible audit trail are the baseline.
Run continuous checks against SOC 2, SOX, or GLBA requirements, surfacing alerts a team will actually act on.
Pull data across systems, generate reports on schedule, and flag anomalies before a deadline.
Pull key terms and risk flags from vendor and customer contracts, checking them against a standard playbook.
Shaped around how each industry here actually works. regulated BFSI.
How we build, every step of the way.
The engineering discipline behind every build.
Scope gets written down before anything else happens: what the agent handles on its own, what needs a person, and what gets logged no matter which way it goes. A stop-and-ask threshold is part of the build from day one, not bolted on once something goes wrong.
Before the first line of production code, a sample from your actual data gets pulled, and the target accuracy gets settled. That number exists before the build starts, not as something discovered after launch.
Every connector reports back on retries and failure rates into a single place your team can check without asking us. Integration health never sits buried where only the build team can see it.
Review checks run alongside the agent from the start, which means a dip in accuracy shows up before a customer or investor would ever need to point it out.
Why teams pick us for this.
A framework does not get picked until the workflow itself is understood. That ordering is deliberate: the architecture follows what is actually found, so the agent is built right the first time rather than reworked partway through.
Every credential, every line of prompt logic, and the vector store itself live in accounts that belong to you, built on open foundations. Nothing about running, changing, or handing this off to a different team depends on us staying in the picture.
An accuracy benchmark, a defined fallback, and monitoring exist from the day the agent ships, which means performance is visible internally long before an investor or customer would have reason to ask.
There is no rotation between the people scoping the work and the people building it, they are the same people throughout, whether the engagement sits inside one pillar or draws on AI as a broader discipline. Nobody hands this off to a junior bench after the first call.
The work moves through four phases, from a mapped process to a live agent, on a schedule that actually holds.
Book a call{ 01 }· 1 to 2 weeks
Inputs, outputs, decision points, and system connections all get written down first; the same groundwork any AI development services engagement needs before a build starts. The output is a ranked scope, a reference architecture, and a timeline that stays fixed.
{ 02 }· two-week sprints
Extraction logic, reasoning, integrations, and monitoring come together across short sprints, each one closing with a working demo on a staging setup that mirrors production closely.
{ 03 }· before go-live
Real data runs through the agent, results get measured against the benchmark set earlier, and every edge case gets closed out before production traffic ever touches it.
{ 04 }· launch and ongoing
The rollout happens in stages with monitoring active the whole time, followed by a proper handover and tuning based on what production actually shows once it is live.
Ways to work
Two ways to bring us in. Either one starts with the same senior engineers handling Agentic AI Development Services in San Francisco from week one.
One workflow, a fixed list of integrations, and acceptance criteria settled before work begins. The price stays fixed, the timeline stays fixed, and the finished system belongs to you outright, a reasonable way to try agentic AI solutions in San Francisco on a single process before committing further. Build versus buy is the underlying question either way, and this model answers it on one contained workflow first.
A senior pod embedded in your existing tools, running two-week sprints across whatever builds and new candidates surface next. This shape tends to suit teams once the first agent is already live and a product, research, or compliance function keeps finding more work to hand off.
Questions, answered.
An agent works through several steps on its own, calls outside tools, and makes decisions based on context to reach a goal without a person guiding each one. A chatbot answers a single question; an agent carries the process itself, exceptions included, the same shift now underway across agentic AI in the US.
Nobody picks a framework before the workflow itself is understood, and that ordering is deliberate. The same senior engineers stay on from the first call to the last sprint, with no rotation to a different team partway through.
A single-agent build typically runs four to eight weeks from assessment to production. A multi-agent system runs eight to sixteen weeks, and the assessment phase settles a firm number before anything gets committed.
Yes. Agents pull metrics from finance, product, and CRM systems, assemble investor updates on schedule, and flag data that does not reconcile before it reaches a board deck, built around the specific reporting cadence a startup already uses.
Yes. Agents extract and structure trial data from multiple sources, check it against protocol requirements, and flag discrepancies for review, matched to the data handling standards biotech and life sciences teams already work under.
Ownership stays with the client, fully. Agent definitions, prompt logic, vector stores, credentials, and cloud infrastructure live in the client's own accounts, on foundations a team can run and modify without needing outside help. We apply the same approach in our Agentic AI development services in Seattle.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
Step 1 - Tell us where you are
Which workflow costs you the most time or carries the most risk. We sign an NDA before any specifics.
Step 2 - Speak to an agent engineer
A senior AI engineer joins within two working days to map your process, your integration landscape, and the shortest path to a working agent.
Step 3 - Get a real plan
A workflow architecture, a scope band, and an accuracy benchmark you can plan against, plus a production system built to last.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
