AiSolvMap out my AI strategy
Map
We check the use case, the data and the systems before writing code.
Who we are
AI built into the operations of $10M-$100M companies.
60+ builds shipped and 1,000+ buyer conversations, with one senior team from the first strategy call to production.
Map out my AI strategy ↗PARTNERS
SAP PartnerEdgeBuild partner
MicrosoftPartnerMODELS AND PLATFORMS WE BUILD WITH
Anthropic Claude
OpenAI
Google Gemini
Meta Llama
Mistral AI
Deepgram
Amazon Web Services
Microsoft Azure
Google CloudMicrosoft Fabric
PostgreSQL
CloudflareWe use whichever model fits the job
No single AI vendor gets all our work. We match the model to each task on accuracy, speed and cost, and host it on Azure, AWS or your own cloud account.
SELECTED WORK
- </>LifeSeasonsChargeback handling, retailer AR, warehouse batching and handwritten customer letters
- </>HiloA five-language support agent and a clinician dashboard for a regulated wearable
- </>Everything2GoOne AI across ERP, accounting, call tracking and ads, plus 9,000 sales calls analyzed
- </>myLawCLESearch over 566 recorded programs that points to the exact moment in each recording
- </>Protocol Health ClubLab results turned into clinician-approved reports, synced to the CRM
Strategy to production
From strategy to software that runs every day.
The architect who maps your strategy stays through the prototype, the rollout and the running of it, so nothing is lost in a handoff.
Map out my AI strategy ↗Prove
A fixed-price prototype on your own data, with your money back if it doesn't work.
Ship
Into the tools your people already use, with shadow mode, approvals and training.
Run
We monitor and improve it, or hand it to your team with the code and docs.
CAPABILITIES
AI agents
Finance and operations work, done end to end
Ask about this ›Private AI and RAG
Answers from your own data, with sources
Ask about this ›Customer support AI
Replies, triage and a human hand-off
Ask about this ›Workflow automation
Work carried between your systems
Ask about this ›AI analytics
Your business data in plain English
Ask about this ›ERP and operations AI
Orders, inventory and warehouse
Ask about this ›Marketing AI
Analytics agents and content at scale
Ask about this ›Testing and monitoring
Quality measured before and after launch
Ask about this ›What we've built
Fourteen things we've built inside real companies
Many reuse a pattern we already run in production, which shortens the build and lets you see it working before the full investment.
Map out my AI strategy ↗PICK ONE
Chargeback alerts, handled end to end
An agent that works every chargeback alert as it lands, and only hands a person the cases it shouldn't decide alone.
Each alert is matched to its order and payment, the order refunded, subscriptions cancelled across brands and the card blocked, then the alert is resolved in the fraud platform. Anything above a dollar cap, or anything it can't match, goes to a person by email with a link straight to the case.
BUILT ON OUR APPROVAL-GATE PIPELINE
The verify-then-execute pattern we use across finance builds: the agent proposes, a cap or a person approves, and every step is logged.
WHAT YOU GET
- Refund, cancel and block in one pass
- A dollar cap on what it does alone
- Exceptions emailed with a deep link
- An audit trail for every alert
Shipped for LifeSeasons: alert handling went from 90 minutes a day to 10, with more than 91% automated.
KountAuthorize.netWooCommercePythonPostgreSQL
Get a strategy for this ›Retailer remittance matching
Remittances read, matched and posted, with every short-pay and deduction classified before it reaches the books.
Grocery and retail chains pay in their own formats, often scanned PDFs, and short-pay by pennies across hundreds of invoices. The workbench reads each remittance, matches lines to open invoices with fuzzy matching for typos, classifies short-pays versus deductions, and lets your AR team review before anything posts.
BUILT FOR REVIEW, NOT BLIND POSTING
Your AR team sees every match, edits write-off accounts and exports disputes, then posts in one step, all or nothing.
WHAT YOU GET
- A parser per retailer, scanned PDFs included
- Fuzzy matching for typos and partials
- Short-pay versus deduction classification
- Posting that can never run twice
Shipped for LifeSeasons: 132 of 132 invoices found on a real remittance, 131 matched to the penny.
Sage 100QuickBooksOCRPython
Get a strategy for this ›Warehouse order batching
Orders grouped into the right carts automatically, five times a day, by rules your warehouse team can read.
The batcher pulls open orders from the warehouse system, scores them by cart, box size, brand and ship-by date, and recreates them as batches assigned to carts. A comparison report showed old versus new batching across about 200 runs, which is how the change was agreed with the floor team.
MEASURED BEFORE IT REPLACED ANYTHING
The new logic was scored against historical runs, cart by cart, before the switch.
WHAT YOU GET
- Runs on a schedule, every weekday
- Rules by cart, box, brand and ship-by date
- Old-versus-new comparison report
- A reconciliation failsafe on every run
Shipped for LifeSeasons: 98-99% success batching about 2,000 Monday orders, built in about three weeks.
WarehouseOSAzure FunctionsPythonSQL Server
Get a strategy for this ›Customer support with a human hand-off
A support agent that answers from your own content in five languages, and knows when to hand over.
We started with an audit: classifying tickets, reading over a thousand negative ratings and building a regional test set. The agent answers from your help content, checks its own confidence, and escalates to a person with the conversation attached. In a regulated business, answers are tested before customers see them.
STARTS WITH AN AUDIT
Ticket taxonomy, an automation audit and a test set from real conversations, before a single answer goes live.
WHAT YOU GET
- Grounded in your help content
- Confidence checks and human escalation
- Tested on a regional question set
- Rolled out market by market
Shipped for Hilo, a regulated medical-device company: customer-experience score from 59.5% to 68.8% in one week, five languages live.
IntercomOpenAIEval harnessFastAPI
Get a strategy for this ›Reply drafting for email and chat
Every customer email and chat drafted from your knowledge base, for an agent to approve and send.
The assistant reads the incoming message, retrieves the right policy or product answer and drafts the reply in your brand's voice. Agents edit and send, so quality stays in human hands while the typing disappears.
OUR ORIGINAL BUILD
The first production agent we shipped, still in daily use.
WHAT YOU GET
- Retrieval over your knowledge base
- Drafts in your brand voice
- A person approves every send
- Works inside your existing helpdesk
Shipped for NeuroQ. In the client's words: “The result has been pretty great.”
FreshchatpgvectorClaude
Get a strategy for this ›Search that cites its source
Answers from a library of recorded programs, each pointing to the exact moment it came from.
Hundreds of recorded programs were transcribed, speakers identified and everything indexed. Members ask a question inside the existing website and get an answer linked to the recording at the right timestamp, so every claim can be checked. A 261-question test set ran before launch.
EVIDENCE OR NOTHING
If the library doesn't support an answer, the assistant says so instead of guessing.
WHAT YOU GET
- Transcription and speaker identification
- Citations to the exact timestamp
- Embedded in your existing website
- Tested on 261 questions before launch
Shipped for myLawCLE: 566 programs processed, 91% of speaker segments identified automatically.
FastAPIPostgreSQLDockerWordPress
Get a strategy for this ›Lab results to a clinician-approved report
Blood-work PDFs turned into a branded clinical report that a clinician signs off before the patient sees it.
The pipeline extracts values from lab PDFs, checks them against marker ranges an admin can edit, drafts the analysis and renders a branded report. A nurse practitioner reviews and approves it on a dedicated page, then it syncs to the CRM.
AI DRAFTS, THE CLINICIAN DECIDES
The highest-return unlock for a clinic whose capacity is limited by clinician time.
WHAT YOU GET
- PDF extraction and parsing
- Editable marker ranges
- Clinician review and approval page
- Separate development and production
Shipped for Protocol Health Club, deployed on AWS with separate development and production environments.
AWS BedrockFastAPIpdfplumberGoHighLevel
Get a strategy for this ›Your business data, in plain English
Ask questions about the business and get charts back, over live company data.
Instead of raw database access, we build purpose-made tools over your ERP, warehouse and ecommerce data, so questions like re-order rate by cohort, retention by channel or units expiring in 90 days get answered correctly. Every tool is tested before staff use it.
HIGHWAYS, NOT DIRT ROADS
Purpose-built, tested tools for the questions your team actually asks.
WHAT YOU GET
- Plain-English questions, answered with charts
- Read-only access to live data
- Tested tool by tool
- Access by role
Shipped for Everything2Go: 247 of 247 read-only ERP tools passed testing before staff used them.
Claude Agent SDKMCPPostgreSQLAzure
Get a strategy for this ›Sales call analysis at scale
Every recorded sales call analyzed, so leaders see what wins deals and what stalls them.
Call recordings are transcribed and scored against the questions your sales leaders care about: objections, pricing pushback, competitor mentions and next steps. Patterns that would take a manager weeks of listening show up in a report.
CHEAPER THAN AN AFTERNOON OF LISTENING
Thousands of calls cost a few hundred dollars to analyze.
WHAT YOU GET
- Transcription at scale
- Custom scoring per call
- Patterns across reps and products
- Exports your team can use
Shipped for Everything2Go: 9,000 sales calls analyzed for a few hundred dollars.
WhisperClaudeCall tracking
Get a strategy for this ›Marketing analytics agent
An agent over Google Analytics, Search Console and Google Ads that answers performance questions and drafts reports.
Marketing questions usually mean three exports and a spreadsheet. The agent queries each platform directly, runs the analysis in a sandbox and drafts the report, with every step traced so you can see how it reached the answer.
EVERY STEP TRACED
Tracing on every tool call, so any answer can be audited.
WHAT YOU GET
- GA4, Search Console and Google Ads
- Analysis in a secure sandbox
- Report drafting
- Full tracing
Built for Everything2Go across six web properties, running on Azure Container Apps.
GA4Google Ads APILangfuseAzure
Get a strategy for this ›Personal letters from customer calls
A personal, handwritten letter after a customer call, written in the founder's voice.
Call recordings are transcribed, the customer's concern pulled out and a letter drafted in the founder's voice with the most relevant customer story attached. An AI reviewer blocks anything off-brand before a robot handwrites and mails the card.
AN AI REVIEWER ON EVERY LETTER
Letters that fail the check are blocked automatically, not sent and regretted.
WHAT YOU GET
- Call transcription and concern extraction
- Drafting in the founder's voice
- An automatic quality gate
- Scheduled handwritten delivery
Shipped for LifeSeasons: real letters going out since August 2026.
WhisperClaudeRedis queuesReact
Get a strategy for this ›Data audits across systems
Product records cross-checked across the ERP, the warehouse system and the shipping platform, with every mismatch listed.
When the same SKU lives in four systems, they drift apart. The audit app compares them field by field and lists every mismatch for someone to fix. It replaced a manual audit before it started.
RE-RUN WHENEVER YOU NEED IT
After every catalog change, not once a year.
WHAT YOU GET
- Field-by-field comparison
- ERP, warehouse and shipping data
- A mismatch list with owners
- Re-runnable on demand
Shipped for LifeSeasons across Sage 100, WarehouseOS and ShipStation.
Sage 100WarehouseOSShipStationSQL Server
Get a strategy for this ›Shadow mode before you switch
A read-only copy of your automations that records what it would have done, before it does anything.
Before cutting over CRM workflows, the new service runs alongside the old one, reads the same events and logs its decisions without writing anything. You compare the two, fix the differences, then switch.
NOTHING WRITTEN UNTIL YOU SAY SO
A write guard blocks every change while in shadow mode.
WHAT YOU GET
- Reads the same events as production
- Logs every decision
- A write guard during shadow mode
- A reconciliation report
Built for a roofing software company's GoHighLevel workflows.
GoHighLevelFastAPIPostgreSQL
Get a strategy for this ›A company-wide Claude rollout
Claude for every team, connected to company systems, instead of everyone signing up on their own.
We set up the company account, shared connectors to company systems, role-based access and a prompt library, and keep it model-agnostic so you're never locked in. People get useful answers from day one because the connectors already know where the data lives.
THE FIX FOR “WE BOUGHT IT AND NOBODY USES IT”
Seats become useful once they're connected to your systems.
WHAT YOU GET
- Company account and admin setup
- Shared connectors to your systems
- Role-based access
- Prompt library and training
In progress for LifeSeasons as the company grew from 70 to 150 employees.
ClaudeMCPAccess control
Get a strategy for this ›Technology
The tools behind our builds
Every choice is made per project and revisited as better options appear. You are never tied to one vendor.
Map out my AI strategy ↗We pick the model for each task on accuracy, speed and cost per task. Switching later is a settings change, and it gets retested before it goes live.
In production: a five-language support agent, checked against a regional question set before launch.
HOW WE DECIDE
We start with the smallest model that passes your tests and only step up where it falls short.
CommercialClaude · GPT · Azure OpenAI · AWS Bedrock
Open-weightLlama, served through Groq
SpeechWhisper · Deepgram
RoutingA primary and a standby model per task
We give every agent a defined job, a short list of tools and a log of everything it does. Anything that changes a record waits for approval, and spending limits are agreed before go-live.
In production: a chargeback agent with a dollar cap that handles more than 91% of alerts on its own.
HOW WE DECIDE
Steps that can be undone run on their own. Anything that changes records, spends money or reaches a customer gets a human sign-off.
FrameworksClaude Agent SDK · Claude Code · OpenClaw
ConnectionsModel Context Protocol servers · REST · webhooks
ControlsApproval steps · action logs · spending caps
SafetyPrompt-injection filtering · scoped permissions
When an answer is wrong, the cause is usually the document it was given, not the model. So we test search quality on its own before tuning any prompts, and attach the source to every answer.
In production: search over 566 recorded programs, each answer linked to the moment it came from.
HOW WE DECIDE
If the AI needs to know your facts, we use retrieval. If it needs to behave differently, we consider tuning. It's usually the first.
Storespgvector · PostgreSQL
MethodsHybrid search · re-ranking · citations
SourcesDocuments · recordings · help centers · catalogs
FallbackSays so when the sources don't cover it
Callers hang up quickly, so voice gets the most testing. We compare voices and response times on your own call scripts before anyone hears one.
Built: a call screener that holds and qualifies callers who used to hang up after about 45 seconds.
HOW WE DECIDE
Voice, speech and model are chosen together for each use case, judged on delay, naturalness and cost.
PlatformRetell
Speech to textDeepgram · Whisper
VoicesFish Audio · Rime · MiniMax
ReasoningClaude · Llama via Groq
AI never queries your ERP directly. A governed sync copies what's needed into a store every tool reads, so your system of record never slows down and the data stays current.
In production: a governed sync from Sage 100 into PostgreSQL, plus a Synapse warehouse moving onto Microsoft Fabric.
HOW WE DECIDE
Data problems sink more AI projects than model problems, so we look at your data first.
SourcesSage 100 · NetSuite · QuickBooks · Shopify · SQL Server
StoragePostgreSQL · Microsoft Fabric · Synapse
PipelinesScheduled syncs · Azure Functions · Redis queues
ChecksMismatch audits across systems
We host it where your IT team wants it, release new versions only after they pass the test set, and trace every step once it's live.
In production: one client's agent platform across 23 container apps, and a 261-question test set gating a legal knowledge launch.
HOW WE DECIDE
Your cloud account if policy requires it, managed hosting if speed matters more. We lay out the trade-offs and you choose.
CloudAzure · AWS · Cloudflare · Vercel · your account
TestingTest sets with AI graders · visual regression tests
VisibilityTracing · action logs · cost per task
EnvironmentsSeparate development, staging and production
Choosing the approach
Retrieval, fine-tuning or better prompts?
These fix different problems and cost different amounts to run. Picking the wrong one early can cost months, so we decide it with you up front.
Map out my AI strategy ↗| Situation | Approach | Reason |
|---|---|---|
| Your information changes often | Retrieval | Retrieval always reads the latest version. A tuned model is frozen at the moment it was trained. |
| Every answer needs a source | Retrieval | The passage comes back with the answer, so anyone can check it. |
| Different people may see different data | Retrieval | Permissions are checked on every question, person by person. |
| Output must follow a strict format or tone | Prompting, then fine-tuning | Instructions and examples first. Tuning only if that falls short. |
| High volume makes each call's cost matter | Fine-tuning | A smaller tuned model can be cheaper and faster per call. |
| The AI has to take actions | An agent with tools | A limited set of tools, approval steps and a log, not a bigger model. |
| You haven't measured where you are today | Prompting first | Quick to try, and it sets the baseline everything else is judged against. |
Ways to work with us
Start with a strategy, go as far as it pays
A prototype, a full build or ongoing support. You decide after each step.
Map out my AI strategy ↗Strategy
Your idea worked into a clear strategy: what holds up, the pitfalls and where to start. In writing or on a call.
Ask about this ›Fixed-price prototype
A working version on your own data before you commit to a full build, with your money back if it doesn't work.
Ask about this ›Build and run
We build it into your systems, then monitor and improve it month to month.
Ask about this ›Alongside your team
Our engineers join your team's repositories and sprints, so the know-how stays in-house.
Ask about this ›Company-wide AI rollout
Claude or ChatGPT for every team, connected to your systems, with access by role and hands-on training.
Ask about this ›Fractional AI lead
Ongoing help with AI strategy, architecture and build-or-buy calls, from the architect behind our builds.
Ask about this ›Industries
Where we've shipped AI
Nine places we've built it, across companies that sell products and companies that sell services.
Map out my AI strategy ↗PRODUCT COMPANIES
Supplements and D2C health
For LifeSeasons: chargeback handling cut from 90 minutes a day to 10, retailer remittances matched to the penny, and about 2,000 Monday orders batched at 98-99% success.
Get a strategy for your industry ›PRODUCT COMPANIESB2B ecommerce
For Everything2Go: one AI across ERP, accounting, call tracking and ad platforms, with 247 of 247 tools tested, and 9,000 sales calls analyzed.
Get a strategy for your industry ›PRODUCT COMPANIESMedical devices
For Hilo, a regulated wearable company: a five-language support agent that took the customer-experience score from 59.5% to 68.8% in a week.
Get a strategy for your industry ›PRODUCT COMPANIESWholesale and distribution
Retailer remittances, short-pays and deductions reconciled across grocery chains, and orders routed across channels.
Get a strategy for your industry ›PRODUCT COMPANIESFood, beverage and CPG
Casa Maestri, a tequila brand: “This is now 1 less bottleneck at my company.”
Get a strategy for your industry ›SERVICE COMPANIESLegal and professional education
For myLawCLE: search across 566 recorded programs that points to the exact moment in each recording, tested on 261 questions first.
Get a strategy for your industry ›SERVICE COMPANIESTelehealth and clinics
For Protocol Health Club: lab results become branded reports a nurse practitioner approves before they sync to the CRM.
Get a strategy for your industry ›SERVICE COMPANIESHome services software
For a roofing software company: a contractor app inside the CRM the contractors already use, switched over with shadow mode.
Get a strategy for your industry ›SERVICE COMPANIESAgencies and real estate
Reply42: “He thinks like a true business partner.” Westrom Group: “Head and shoulders above the others.”
Get a strategy for your industry ›Integration
AI integration services
We wire AI into the data, permissions and software your company already uses. A demo proves the model can answer. Production means it reaches your data, follows your access rules and writes back without breaking anything.
Map out my AI strategy ↗Seven things we get right in every integration
Reading data without slowing the ERP
AI reads from a governed copy of your data, refreshed on a schedule, never from the live ERP. Nothing has to be migrated before you start.
Access that matches your org chart
Each person can only reach, through the AI, what they could already open themselves. Rules are set per employee, per system and per action, inside the tools.
Writing changes back safely
Every change is logged with what triggered it, can only run once, and has limits, such as a cap on refunds an agent can issue alone. Records, money and customer contact go through a person by default.
Shadow mode before cutover
The new automation runs read-only beside the old process and records what it would have done. You compare, fix the gaps, then switch.
Where it runs
Managed hosting or your own cloud account. We explain what changes with each and you pick.
Keeping costs visible
Usage and cost per task are tracked from week one, with caps agreed before launch.
Model updates
AI vendors change their models on their own timeline. Every candidate model runs against the same test set before it replaces the current one.
Systems we've connected AI to
NetSuite
Sage 100
QuickBooks
Shopify
WooCommerce
Amazon Seller CentralShipStationWarehouseOSAuthorize.netKount
HubSpotGoHighLevel
IntercomFreshchat
Trustpilot
Google Ads
Google Analytics 4
Search Console
WordPress
SQL ServerPostgreSQLMicrosoft Fabric
Excel and SharePoint
Slack
Gmail
NotionREST APIs and webhooksModel Context ProtocolQuestions worth asking before you hire anyone
- Do you need direct access to our ERP, or a copy of the data?
- How do you stop a bad change from reaching our books?
- What will it cost to run each month at our volume?
- Who do we call when something breaks?
Proof
Results from our AI implementation work
Three builds with numbers you can check, from a supplement brand, a B2B ecommerce company and a regulated medical-device maker.
Map out my AI strategy ↗
CASE 01 · FINANCE AND FULFILLMENTGet a strategy like this →
A $100 million supplement brand
Chargeback alerts were worked by hand every morning, weekends included. Retailer remittances were matched line by line and posted through a 41-click process. One warehouse veteran batched every Monday from memory.
90 → 10minutes a day on chargebacks, more than 91% automated
132 / 132invoices found on a real remittance, 131 matched to the penny
98-99%success batching about 2,000 Monday orders
A $50 million B2B ecommerce companyCase 02 · Data, Sales And Service
Their ERP, accounting, call tracking and ad platforms each held part of the picture, so every answer meant an export. Someone had told them AI meant buying a GPU server.
Get a strategy like this ↗A regulated medical-device companyCase 03 · Customer SupportSupport ran on a rigid decision-tree bot, and the helpdesk's own AI failed their testing. In a regulated business, one wrong answer is a real risk.
Get a strategy like this ↗Your companyNextTell us what you're considering and we'll map out where AI fits first.
Map out my AI strategy ↗Testimonials
Operators who handed us the work
What founders, CFOs and product leaders say after working with us.
Map out my AI strategy ↗
“Aditya and Dhruv at AiSolv were incredible. Easy to work with, thoughtful, they cared, and very talented. They did not treat this as ‘my’ project, but performed like it was ‘our’ project.”
“Had a great experience with Dhruv, he was great at communicating and understood what I needed for my project. This is now 1 less bottleneck at my company, and I’m grateful for it. Thank you!”
“Dhruv was a fantastic asset to our team and delivered quality work on time and communicated at all times very well to ensure we got the outcome we needed. Highly recommended and would definitely work with them again.”
“What a blessing it was to find Dhruv. I have hired over 30 or 40 vendors and he is head and shoulders above the others. Very professional, timely and the quality of the work is beyond good, it’s outstanding.”
“Game-changing AI partner! Dhruv doesn’t just deliver, he thinks like a true business partner, deeply invested in our success. Fast, innovative, and genuinely focused on helping our business grow.”
“Extremely thorough, detail-oriented, and brings a forward-thinking, strategic mindset to every part of the project. He doesn’t just execute, he elevates the overall solution.”
Choosing a partner
What to look for in an AI partner
How we protect your data, keep the AI reliable and keep the project on track.
Map out my AI strategy ↗Security
Your data stays protected without slowing every decision down.
- Under NDACovered from the first message you send.
- Your cloud, your billIt can run in your own cloud account.
- Access by rolePer employee, per system and per action.
- Separate environmentsDevelopment, staging and production kept apart for regulated work.
AI safety
Checks that keep answers accurate and actions safe.
- Test sets and AI gradersAccuracy measured before anyone relies on it.
- Answers from your sourcesOr it says it doesn't know.
- Prompt-injection filteringCustomer-facing tools screen inputs before acting.
- Human sign-offOn anything that writes, spends or contacts a customer.
Delivery
The same senior people from start to finish.
- The architect stays onThe person who mapped your strategy works on the build.
- Weekly check-insProgress, decisions and risks, every week.
- More than one engineerSomeone else already knows your system.
- You own itCode, data and documentation are yours from day one.
Six questions to ask any AI partner, including us
- Can we see something working on our own data before we commit?
- Where does our data go, and who can see it?
- What will the AI do without a person approving it?
- How do you measure whether it's accurate?
- What happens to the cases it can't handle?
- If we stop working together, can we keep running it?
FAQ
Frequently asked questions about our AI strategy work
The questions that come up before people send us their plans.
Map out my AI strategy ↗Q:What if I don't have a plan yet?
Send the goal, or just a question. We'll tell you where we'd start and why. A plan is better, but it isn't required.
Q:Is this a sales pitch in disguise?
No. You get a clear strategy for what you're considering. No call required and no string of follow-up emails. If the strategy is all you needed, that's the end of it.
Q:What if we see it differently?
Then we'll tell you what we'd change and why, before you spend months and a budget finding out. And we'll say just as plainly when your thinking is right.
Q:You don't know my industry.
Every industry has its quirks, but the business functions repeat. We may not have built your exact thing in your industry, but we've built it elsewhere, and we may have worked in your industry on something else. We bring both, then pick up the rest from your team without getting in the way of their day.
Q:Our data is messy and our systems are old.
That's the normal case, and it's why to ask us. We've worked across the major clouds, many ERPs, on-prem and off.
Q:We're already talking to a big consulting firm.
Then talk it through with us as well. We carry no layers of middle management or sales machinery, and the person you work with is the person who would build it.
Q:Is it safe to share our plans with you?
Yes. Everything you send stays under NDA. And when we build, your data doesn't have to leave your premises or your own cloud account.
Q:What happens if we want you to build it?
The first step is fixed-price, with your money back if it doesn't work, and you see the scope before you commit to anything. You own the code, the data and the cloud account.