Most AI vendors sell you a pilot, then hand you off to a bench of juniors.
One senior team builds it all: and stays through production.
Knowledge graph architecture, agentic automation, embedded engineering, AI audits: six capabilities, one team, no hand offs.
✓No obligation. Every engagement starts with a confidential conversation, not a sales script.
587+ AI products shipped · 12+ years in production · 95% client retention
Services
Become part of a team that values innovation, professional growth, and technical excellence. We provide the environment and tools you need to build world class digital solutions while advancing your career.
The stack
The AI stack behind
the guarantee
Five layers, one senior team. A knowledge graph core, model agnostic reasoning, and enterprise grade compliance: deployed on your cloud, owned entirely by you.
Layer 01
AI & reasoning
Grounded generation with a citation trail on every answer: model agnostic, so you're never locked to one vendor's roadmap.
Layer 02
Knowledge & data
A knowledge graph is the difference between AI that guesses and AI that knows. This is the layer regulators audit and traders trust.
Layer 03
Agents & orchestration
Autonomous workflows that reason, act, and escalate correctly: wired to the tools your team already runs on.
Layer 04
Cloud & infrastructure
Deployed in your VPC, on your cloud. We meet your infrastructure where it is: no forced migrations, no lock in.
Layer 05
Trust & compliance
Security architected in, not bolted on. The controls enterprise buyers require, documented before the first line ships.
Industries
Eight industries. One senior team.
The same knowledge graph foundation, tuned to the rules, data, and risk profile of your sector.
Fintech
Payments that move billions
Payment platforms, core banking systems, and AI powered transaction processing. Audit ready infrastructure built for scale.
How a regulated AI deployment runs
Six stages engineered for strict compliance, from day one. The first two stages are a no obligation Architecture BriefingFintech · Healthtech / EHR: fixed scope, staged commitment, and outcomes agreed in writing before we start.
LLM & Data Discovery
We map core enterprise systems (payment ledgers, EHR/EMR databases, and legacy APIs) evaluating Claude, GPT 4, and open models for your use case. Secure data lineage and audit trails designed in before any retrieval code is written.
Illustrative: mapped to your stack during discovery
Illustrative: mapped to your stack during discovery
Every engagement · 7 capabilities
What you get, in every engagement.
One senior team, seven disciplines: from architecture through ownership. Click any capability to see how it lands.
AI Architecture
A senior architect designs the retrieval strategy, the grounding layer, and the model routing before any code is written. Model agnostic by default, so you are never locked to a single vendor's pricing or roadmap: and the system survives the next model generation without a rewrite.
Engagement flow · 3 stages
Ground it. Prove it. Scale it.
Three stages, one senior team, one knowledge graph that grows with you. Every stage earns the next: you leave whenever the eval numbers stop justifying more scope, with every deliverable yours to keep.
Architecture & AI Discovery
We audit your data landscape, evaluate LLM models against your use case, and map retrieval architecture: all before writing a single line of code.
AI deliverables
AI Proof of Concept
One AI workflow shipped into production with evals, guardrails, and citation trails: the moment your team sees grounded AI work on real data.
AI deliverables
Long Term AI Development
Same senior team scales the AI surface: continuous evals, model optimization, and quarterly architecture reviews keep the system sharp.
AI deliverables
95% of clients extend past Stage 2.
Stage 01
Architecture & Discovery Session
Most AI budgets get spent finding out what’s broken: not fixing it.
A senior architect reviews your stack and hands you a written findings report: the same review that has surfaced $50K+ in reclaimable spend in every engagement so far. Confidential, 30 minutes, and yours to keep whether or not we ever work together.
Written findings report
AI Opportunity Audit
Summary
12
Systems reviewed
7
Findings logged
3
Quick wins
$352K
Annual value
4 mo
Payback
$0
Your cost
Findings by lane
| Lane | Where it leaks | Evidence | Reclaimed | Annual value | |
|---|---|---|---|---|---|
| F-001 | Enterprise | Compliance reporting overhead | Quarterly, fully manual across 3 teams | 1,400 hrs / yr | $92K |
| F-002 | Startups & scale ups | Engineers on data plumbing | 40% of sprint capacity, not product | 2,100 hrs / yr | $138K |
| F-003 | Brands & multi location | Fragmented customer records | Shopify, Zendesk & POS disagree | 980 hrs / yr | $68K |
| F-004 | Small teams | After hours lead response lag | 9pm leads answered next morning | 760 hrs / yr | $54K |
| Total | 5,240 hrs / yr | $352K | |||
Sample finding, in full
“Three analysts spend the first two weeks of every quarter pulling the same figures from four systems into one spreadsheet.”
What we observed
- 1.Figures sourced manually from 4 disconnected systems
- 2.No lineage: auditors cannot trace a number to source
- 3.Two full re runs last year after a mismatch was found
What we would change
- 1.Model the four sources into one knowledge graph
- 2.Generate the report with a citation on every figure
- 3.Auditors click any number and see its lineage
Map where AI pays
Ranked by value, not by hype
Documented savings
Every figure traced to a system
An honest no
What should not be built, and why
A portable plan
Take it to any vendor you like
Stage 02
Proof Of Concept
A working system on your data, in your environment: not a slide deck. Six weeks from kickoff, your stakeholders judge something real.
Query
Which Q3 transactions breached the settlement SLA?
14 transactions exceeded the 4 hour settlement window, concentrated in the EU rail on Sept 12 to 14. Root cause traced to a retry backoff on the acquirer callback.
96%
Accuracy
Fewer
Hallucinations
100%
Cited
1.4s
P95 latency
Measured against a 240 case eval set built from your own historical queries.
Build plan
What happens, week by week
Model the graph
Entities, relations and rules from your real data
Ground the answers
Retrieval, citations and the eval set
Harden it
Guardrails, fallbacks, injection defense
Ship to your VPC
Deployed, monitored, handed to your team
Fixed scope, staged commitment
Outcomes agreed in writing before week one begins.
Same six weeks
What ships, in your lane
Audit ready GraphRAG
Citation trail on every answer, deployed in your VPC.
The AI feature investors asked for
Embedded squad ships it inside your repo, not beside it.
One unified customer graph
Shopify, Zendesk, GA4 and POS reconciled in real time.
A pipeline that answers at 9pm
GHL and n8n wired end to end: you never touch a setting.
Stage 03
Long Term Development
An embedded senior squad that scales with you: it transfers knowledge as it goes. 95% of our clients extend, but you are never locked in.
Embedded squad
Inside your repository
Solutions Architect
Owns the graph model
Product Manager
Runs the sprint cadence
Product Designer
Designs the trust surfaces
Full stack Engineers
Ship into your codebase
DevOps Engineer
Owns CI/CD, IaC and uptime
Evaluation Engineer
Guards accuracy and drift
Backup Engineer
Already knows your system
Customer Success
Owns the relationship
You own 100% of the code and IP
Knowledge transfers as we go: no lock in
Operating rhythm
How you stay in control
Standup + async written update
You see blockers the day they appear
Sprint review & priority reset
Scope changes are cheap when caught early
Eval report & drift audit
Accuracy measured, not assumed
Architecture & cost review
We flag what you should stop paying for
Owned by DevOps
Every environment, reproducible from code
Development
Synced- Last deploy
- 12 min ago
- Replicas
- 1
- Database
- t4g.micro
Staging
Synced- Last deploy
- 2 hr ago
- Replicas
- 2
- Database
- t4g.small
Production
Synced- Last deploy
- Yesterday
- Replicas
- 3 · multi-AZ
- Database
- t4g.small + RR
No snowflake servers. Infrastructure lives in your repo as code, so a new environment is a pull request: not a ticket.
Owned by Design
The surfaces that make AI answers trustable
48
Components
120
Design tokens
AA
WCAG 2.1
100%
Figma parity
Trust surfaces
Citations, confidence and sources: the interface work that makes a model's answer defensible to an auditor.
Uncertainty states
What the product says when the model is unsure, slow, or wrong. Designed deliberately, not left to a spinner.
Specs engineers can build
Tokens, props, states and breakpoints handed over with the design: nobody guesses a padding value.
New screens inherit the system automatically, so the tenth feature looks like it shipped with the first.
Same senior team
How the engagement is shaped, by lane
Dedicated pod, quarterly roadmap
Architect, designer, DevOps and engineers held against a governance calendar. Security reviews and audit evidence produced as a matter of course.
- Squad
- 5 to 8 senior
- Cadence
- Quarterly
- Exit
- Full handover pack
Embedded squad, sprint by sprint
We work inside your repo and your standups. Scale the squad up before a raise, down after it: no severance, no bench.
- Squad
- 2 to 4 senior
- Cadence
- 2-week sprints
- Exit
- 30 days notice
Graph stewardship + growth work
One team keeps the customer graph healthy as you add locations, channels and systems: and ships the personalization on top of it.
- Squad
- 2 to 3 senior
- Cadence
- Monthly
- Exit
- Docs + training
Managed pipeline, done for you
We own the automation end to end and report on what it produced. You never open a settings panel or debug a webhook.
- Squad
- Managed
- Cadence
- Monthly report
- Exit
- Cancel anytime
Client results · Regulated verticals
Clients about Agentic Giants
From payment platforms to patient records: the same senior team, audited by both engineering and compliance.
“Our compliance team went from blocking every AI feature to approving them: because now every answer has a source.”
Head of Data·RYVYL
Measurable hallucination reduction in production“The knowledge graph handles PHI the way our auditors want: every retrieval cites the source record, and nothing bypasses HIPAA logging.”
VP of Clinical Systems·Regional health network
HIPAA audit ready · zero findingsGlobal fashion retailer
+30% average order value
One customer graph across Shopify, Zendesk & GA4 powering real time personalization.
D2C wellness startup
AI MVP in 6 weeks: seed round closed
Embedded senior squad shipped the recommendation engine investors asked for.
Verified Outcomes · 4 Lanes
The proof your board
asks for.
CTOs, VPs and founders shortlist on outcomes, not decks. Four lanes, four verified results: production deployed, compliance audited, and measured against your own baseline.
Audit ready AI for compliance teams. Every answer cites its source: SOC 2, HIPAA, PCI DSS gated.
Production evidence
AI MVP shipped, seed round closed. Senior team, no data hires needed to launch.
Production evidence
One customer graph across Shopify, Zendesk & GA4. Real time personalization powering every touchpoint.
Production evidence
Voice + SMS AI on autopilot. Books meetings, answers questions, never sleeps: under a solo owner budget.
Production evidence
12+
Years in production
95%
Clients extend engagement
50M+
Users in production
Every outcome above is measured against your baseline, not ours: with written sign off before we start.