You have a deep tech idea. It feels bold. It keeps you up at night. But you do not want to write code yet. You want to know if it is worth your time. You want proof. You want a clear path. This guide is for you.
Define the real problem before you touch the tech
Most teams rush to describe features. Slow down and capture the situation as it is today. Sit with the work where it happens. Watch the shift change, the handoff, the scramble after an alarm. Write down exact words people use when things break.
Keep timestamps. Note who gets called first and who makes the final call. These details reveal the true shape of the pain and how it spreads across roles. When you later share your findings back to the team in their own words, you build trust and gain access to better data.
Build a problem charter that survives scrutiny
Create a one page note that names the problem owner, the business goal that suffers, the current workaround, and the single event that triggers action. Tie every claim to a source, such as a log file, a ticket ID, or a cost line from finance.
Add two constraints that cannot change, like approved devices or data residency. These constraints guide your future choices and prevent dead ends. End the page with the smallest test you could run next week inside current rules.
When the charter is short and grounded, busy leaders read it, correct it, and sponsor your next step.
Map the time profile of the pain rather than the averages. Ask when it spikes, when it is quiet, and what seasonal or batch patterns exist. Many deep tech wins come from hitting narrow windows, not broad changes.
If the worst hour is 3 p.m. on Mondays, design your pilot there. If the largest scrap occurs during tool warm up, target only that phase first. Precision like this reduces scope while raising impact.
Treat alternatives as direct rivals. Document what people actually do today, even if it looks crude. A laminated checklist can beat a model if it is faster and trusted. Shadow the workaround and measure it with a stopwatch.
Time to detect, time to repair, and time to recover tell you where software could help and where it would only add steps. If your idea cannot beat the workaround on at least one of those times, reshape the promise.
Quantify cost of delay and prove urgency
Translate the problem into daily loss, not yearly estimates. Use numbers the team already reports. If a bad pick adds twenty seconds and happens three hundred times a day, compute the hours lost and what that means in overtime or missed shipments.
Then ask what happens when the delay hits a compliance deadline or a service level. These edge effects often drive real buying. When you can link your idea to a missed metric on the operations dashboard, you move from nice to have to must have.
Interview for decision triggers, not opinions. Ask for the most recent purchase that looked like your area and what forced it through procurement. Was it a recall, a customer penalty, or a board directive.
Capture the trigger in your charter and align your test plan to recreate its evidence, even in small form. If audits move budgets, design a trial that generates an audit ready record.
Make the problem falsifiable
Write a plain sentence that could be proven wrong with simple observation. For example, unplanned line stops come from sensor drift more than from operator error. Now plan a short observation to check it, such as sampling twenty stop events with cause codes and photos.
If your sentence fails, you saved months. If it holds, you now know where to push. Falsifiable statements keep you honest and help leaders see you as a partner, not a vendor.
Close this loop by scheduling a readout with the problem owner. Bring the charter, the time profile, the cost of delay math, and the falsifiable statement with its first result.
Ask for permission to run the smallest live test that could change next quarter’s metric. With that yes, you have real validation without writing code.
Replace code with clear signals of value
Treat proof like a contract, not a concept. You can win trust by showing how work will change next week, not someday. Build simple artifacts that make the result obvious and the path safe.
Each artifact should answer a hard question a buyer will ask in a steering meeting. Keep it in their language and mapped to their existing reports. When people can see the change on their own dashboards, they act faster.
Start with a plain results memo for one workflow. Describe the start state, the action you propose, the handoff point, and the end state. Add dates and owners. Keep it to a page so leaders can forward it without edits.
Pair this with a two minute screen capture that walks through the steps, using mock screens and real numbers from last week. Short, concrete, and familiar beats a long pitch every time.
Create a tiny ROI calculator tied to their KPIs. Use only fields they already track, like error rate, scrap rate, throughput, or overtime hours. Preload it with last month’s values and let them change one or two inputs.
When they move a slider and see the impact in their units, the value becomes theirs, not yours. Host it in a simple sheet. Share view only first, then offer edit access to the champion.
Replace demos with a dry-run packet that includes a process map, a data readiness checklist, and a day-by-day pilot plan. The process map shows exactly where your method fits and how exceptions route.
The readiness checklist lists the few items they must confirm, such as an S3 bucket name, a log export, or a contact for safety review. The day plan outlines kickoff, data pull, manual analysis, readout, and decision. Busy teams say yes when they see a plan that respects their calendar.
Use a shadow price test to validate willingness to pay without code. Offer two start dates with different fees, framed as a reserved implementation slot. The earlier date carries a higher fee because it displaces other work.
If they pick the earlier date, the pain is acute. If they pick the later date, the pain exists but is not urgent. Either way, you learn before you build.
Turn security from a blocker into a proof point. Draft a short control sheet that maps your proposed workflow to their policies. Note data residency, access scope, retention, and audit logs.
Keep it specific and minimal. Ask their security owner to mark gaps in red. When you return a revised sheet that closes those gaps, you show discipline and reduce review time later.
Turn artifacts into commitments
Give your champion tools they can use inside their org. Write an internal email template from their voice that explains the problem, the proposed change, the pilot dates, and the expected impact on one metric.
Provide a one slide executive summary with the same message and a single chart. Include a short FAQ that handles the top five objections with clear, testable answers. When you supply these, the champion does not need to invent the story. They become your seller.
End each meeting with a dated next step that leaves a mark, such as a calendar hold for the readout or an intro to the budget owner. Send a recap the same day with the memo, the calculator link, the control sheet, and the internal slide.
Ask for a simple reply that confirms the timeline. Each reply is a small commitment. Stack enough of them and you have validation without writing a line of code.
Learn to talk to the right ten people
Your goal is not more meetings. Your goal is the right meetings that change what you build next week. Start by narrowing the field. Pick one industry, one workflow, and one role that lives with the pain each day.
Do not chase titles that only watch dashboards. Aim for people who touch the work, sign the check, or block the change. Ten strong voices from this narrow slice will teach you more than fifty general chats.
Build a tight target list
Create a short profile for the person you must learn from. Include where they sit in the org, the tool they use the most, the metric they wake up to, and the one decision they make that moves money. Search for people who publish shift notes, root cause summaries, or post-mortems.
Those people think in facts and will share them. Ask for intros that are double opt-in so no one is surprised. When you get a no, ask for one peer who fits the same profile. Warm chains grow fast when you are specific.
Earn the meeting by proving you did your homework. In your note, name their process, a recent change you saw in their plant or lab, and a metric they likely track.
Offer a crisp twelve minute call with two questions and a concrete artifact, such as a storyboard or a one page memo. Promise not to sell. Keep the promise. People say yes when they feel seen and safe.
Run the conversation like a field study. Open with their day, not your idea. Ask about the last time the problem showed up and how they knew. Ask what they did in the first ten minutes. Ask who joined the call and why.
Then share a tiny piece of your approach in plain words and watch for the moment they lean in or pull back. If they lean in, dig into where your method fits their stack. If they pull back, ask what would have to be true for this to help next quarter. You are looking for firm edges, not praise.
Use quotas to avoid false learnings
Do not let your ten tilt to fans of new tech. Set small quotas across roles and outcomes. Include a buyer, a user, and a blocker. Include at least three people who told you no. This mix keeps you honest.
A neat story from fans will sink you if the blocker can stop the pilot with one email. When you hear the same words from each group about the same moment in the workflow, you have a signal you can trust.
Turn each talk into a next step that leaves a trace. Ask for a small dataset, a shift shadow, or a calendar hold for a readout. Ask for permission to name them as an anonymous reference when you speak to their peers.
Send a same day summary with their words, your revised value promise, and the smallest test you propose. When two or more offer access, you have early demand. When one offers budget, you have a lead. When none offer anything, change the scope and try a new slice.
Close the loop by tracking a simple funnel. Intros booked, calls held, concrete next steps, and paid pilots. If one stage stalls, fix the message or the target, not the whole idea.
This is how you learn fast without code and move toward proof that buys trust. If you want help turning these talks into strong IP and a pilot plan that gets approved, you can apply anytime at https://www.tran.vc/apply-now-form/
Prove the data path, not the algorithm
Your model will not matter if the right facts do not arrive at the right moment. Show, in plain steps, how raw facts turn into action. Start with one real event from last week and follow it from source to screen.
Note where it was created, how long it took to move, who could see it, and what was done with it. Time each hop. Write down names of systems and owners. This trail is your backbone.
When a leader asks how your idea fits, you can point to the exact places where it reads, transforms, and hands off.
Do not ask for a full dump on day one. Ask for the smallest slice that proves the path. One hour of logs with headers often beats a huge export with missing context. Keep a simple sheet that mirrors their schema.
Add one column for when you first saw each row and one for when a human took action. Latency is where value usually leaks. When you can prove a cut in that delay without code, trust rises fast.
Design a dry run that stresses the ugly parts. Pick a period with noise, shift change, or maintenance. Recreate the key transforms by hand. If a field is blank, show how you would fill it or route around it.
If names change across systems, show the match rule you will use. Record each fix in a small playbook. This becomes your future data contract. It also reveals where to ask the client for a simple change that saves weeks later.
Make the path safe, legal, and ready to scale
Treat privacy and safety as part of value, not as hurdles. Ask which fields are sensitive and propose a way to avoid them. Many wins come from using fewer fields, not more. Show a redacted sample that still supports the claim.
Offer to run the first pass on their site so raw data does not leave their walls. When you bring a clear plan for access, storage, and audit logs, reviews move faster and your champion gains air cover.
Prove that the flow holds up over time, not just in a lucky hour. Pick three small windows from different days and repeat your manual steps. Track drift in field names, units, and ranges.
If drift is high, add a simple guard such as a check that warns when a value looks off. Share the warning rule in plain words. Buyers want less fire, not more features. A tiny guard that avoids a bad alert can be more persuasive than a complex score.
Plan for failure before you build. Decide how the process should behave when data is late or missing. Show the buyer a simple fallback, such as holding the last known good value or sending a short note to a human owner.
Walk through a past outage and explain how your method would have kept work moving. This turns fear into a calm path forward.
Close with a one page data brief. Name the sources, the transforms, the timing, the owners, and the fallback. Attach the small samples you used and the measured delays you cut. Invite the team to correct it.
When they edit your brief, they adopt the path. At that moment, your idea stops being a model and becomes a safe, clear stream that feeds a real result. If you want help turning this stream into claim language that protects your edge while you pilot, you can apply anytime at https://www.tran.vc/apply-now-form/
Design crisp experiments that do not need code
An experiment without code must still be tight. Choose one narrow claim, one place to test, and one clock to measure. Anchor the work to a real baseline from last week, not a guess.
Write down how you will collect evidence and who will judge the result. Set start and stop times so the trial cannot drift. When the window closes, you decide using the rule you wrote before you began.
Start with a clean baseline. Pull a short slice of recent work and lock it. If the flow varies by shift or day, match like for like. Compare Mondays to Mondays, not a calm night to a busy afternoon. If two sites differ, pick one and hold the other for later. Small and fair beats broad and noisy.
Create a manual playbook that anyone on the team can follow. Spell out each step, the data touched, the timing, and the handoff. Use screenshots, photos, and exact file names. Ask a peer to run the playbook once while you watch.
If they get stuck, your design is not crisp yet. Fix it before you test.
Remove bias and noise before they ruin the read
Keep the person who judges outcomes separate from the person who runs the steps. If the claim is fewer false alarms, have another teammate check which alerts were true, using a simple rule you both agree on up front.
When people know the hoped-for result, they nudge the line. Blinding prevents that. If blinding is not possible, at least hide the proposed label until the reviewer has logged a decision.
Plan how to handle missing or late data. Decide in advance what you will do if one input fails for an hour. Hold the last value, skip that window, or flag it as a miss. Write the rule now and stick to it later. A small, firm rule beats ad hoc fixes that bend the score.
Use shadow mode to test fit without risk. Let the manual method run alongside the current process. Do not change the live decisions. Record what your method would have done and compare to what happened.
This shows value and safety at the same time. If the gap is large and steady, you have proof worth showing leaders.
Set acceptance in plain words. Name the number you must beat and the time you must hold it. Add a second check for stability, such as the result must hold on two different days.
When the test ends, write a short page with the setup, the numbers, the caveats, and the next move. If it is a yes, you plan a larger run. If it is a no, you reduce scope or change the claim. Either way, you move with clarity.
Conclusion
You can test a deep tech idea without writing code. You can do it with plain words, small trials, and real numbers. Start with the problem as it is today. Show the path from raw facts to action. Prove value with simple tools.
Ask ten real people who live with the pain. Design small, fair experiments that stand on their own. When the signals line up, you move with confidence. When they do not, you change course fast and save months.