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Ask. Find the path.

Make AI a real productive force for your business

We deliver end-to-end AI solutions for businesses: from pinning down the problem and designing the approach to building, launching and improving it, we bring AI into your workflows, products and services to cut cost and lift efficiency.

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Illustration

Approval

Expense claim · Shanghai trip, 3 nights · Hotel CNY 2,400

  • Hotel is CNY 800 a night, above the tier-1 city limit (per travel policy 3.2).
  • Everything else is within policy.

Approve this expense claim?

ConfirmSend backAwaiting your confirmation
Open the full workbench
  • Human in the loop
  • Quality under control
  • End-to-end delivery

Context

Why now

  • AI can now do real work

    Over the past year AI has changed in substance: from answering questions to reading documents, using tools and finishing multi-step tasks across systems. Much of the work people used to repeat by hand can now be handed to it.

  • Businesses need a new way to run

    Many businesses today run on thin margins, some on none. When growing bigger is not an option, cutting cost, raising efficiency and redesigning how work gets done is the more workable path.

We bring what AI can really do today into everyday business work, to help companies cut cost and work more efficiently.

Capabilities

What we do

Three services, one goal: helping businesses cut cost and work more efficiently.

  • Agent development

    Hand repetitive, multi-step, labour-heavy work to AI agents that use tools, finish tasks and plug into the systems and processes you already run.

    • Scenario and workflow mapping
    • Agent design and implementation
    • Tool and system integration
    • Evaluation and launch support

  • Websites with AI built in

    Make the site serve customers and convert, not just display information: we build AI into search, recommendations, Q&A and conversion paths to lift service efficiency.

    • Designing how AI shapes the experience
    • Front end and AI features built together
    • Data, model and API integration
    • Evaluation and continuous iteration

  • Software with AI built in

    Make core business processes faster, more accurate and less labour-intensive: we bring AI into data entry, review, decision support and cross-system orchestration, cutting repetitive work and human error.

    • Workflow and data mapping
    • AI capability design and integration
    • Front-end and back-end development
    • Evaluation, monitoring and iteration

Business systems

Three capabilities that cover the systems a business runs on, with AI built into each one from day one.

  • CRMCustomers, leads and follow-ups in one place; AI scores leads, summarises conversations and suggests the next step.
  • ERPPurchasing, inventory and finance connected; AI flags anomalies, extracts documents and builds operating reports.
  • OAApprovals, documents and collaboration in one place; AI flags approval risks, writes up meeting notes and answers policy questions.
  • SaaS productsMulti-tenant SaaS built from scratch, with AI as a core capability of the product.
  • Industry systemsPurpose-built systems for property management, insurance, finance and other industries, fitted to how they work.
See solutions

Difference

Why Askpath

AI has made writing code faster, and it has also made rough systems more common. We use AI to move faster, without compromising on quality, experience or security.

  • Clear interaction logic

    One entry point per feature, buttons in the same place across the product; every action gives feedback, with complete empty, loading and error states.

  • Design with a brand

    Restrained design that serves the business information, not the same “AI-looking” interface as everyone else.

  • AI built in deep, and smooth

    AI works inside search, form filling, alerts, approvals and reports, reading and writing business data directly, not as a chat box stuck in the corner.

  • A reliable back end

    Input validation, role-based permissions, retries and logs when something fails, and transactions that keep data consistent.

  • Quality under control

    AI writes the code, people review it: every change has automated tests and code review, and delivery comes with tests and documentation.

The usual quick AI build
How Askpath builds it

What differs

  1. 1

    The usual quick AI buildDuplicate entry points: a “New customer” button in the top bar and another in the sidebar, each doing something different.

    How Askpath builds itOne entry point, one behaviour, buttons in the same place across the product.

  2. 2

    The usual quick AI buildUnclear state: you click Save and nothing tells you what happened.

    How Askpath builds itEvery action gives feedback: success, the reason for a failure, one-click retry.

  3. 3

    The usual quick AI build“AI-looking” colours: purple gradients, glowing buttons, emoji.

    How Askpath builds itRestrained design that serves the business information.

  4. 4

    The usual quick AI buildAI only in the corner: a chat box with no access to business data.

    How Askpath builds itAI in the list itself: natural-language filters, lead scores, follow-up suggestions.

  • A request fails

    The usual quick AI buildThe whole page goes blank and everything you just typed is lost.

    How Askpath builds itIt says why, keeps your input, offers one-click retry and logs the error.

  • Access beyond your role

    The usual quick AI buildChange a parameter in the link and you can see someone else’s customers.

    How Askpath builds itEvery request is checked against your role; out-of-bounds requests are refused and logged.

  • Two edits at once

    The usual quick AI buildTwo people edit the same order and the later save silently overwrites the first.

    How Askpath builds itThe conflict is detected, you are told someone else changed it, and a person decides what to keep.

Demo

AI in every step of the work

Open a system and try what AI can do at each step.

Illustration

CRM Sample customer A: from lead to renewal

Lead intake

Before Sales typed up business cards by hand, then looked up each company before entering it.

Trade-show business card · 1 website form (illustration)

Company
Sample customer A · manufacturing
SizePlease check
About 200 people
Need
Production scheduling and quotes
Source
Autumn trade show

Find customers

Before Filtering list after list, or digging through spreadsheets.

High-intent customers not followed up last month

  • Sample customer A · ManufacturingLead score 86, last contact 34 days ago
  • Sample customer B · RetailLead score 81, no contact since the demo

Manufacturing customers in the east region

  • Sample customer A · SuzhouManufacturing · East region · Negotiating
  • Sample customer C · NingboManufacturing · East region · Signed
  • Sample customer D · HefeiManufacturing · East region · Quote pending

Customers whose contracts are expiring soon

  • Sample customer EContract ends in 30 days, active usage lately
  • Sample customer FContract ends in 45 days, usage down over the last two months

Follow-up summary

Before Scrolling back through chats and call notes to pull out the key points.

6 conversations with sample customer A

  • Delivery time matters most; they want to go live within 8 weeks.
  • They have questions about the quote and want to know about phased payment.
  • They would like a product demo next week.
  • Next step: schedule the demo and include a phased quote.

Quote

Before Working out discounts by hand from the price list, writing the quote, then chasing a manager to approve it.

Sample customer A · phased quote draft

Phase 1
Scheduling module · 8 weeks · CNY 180,000
Phase 2
Quote management · 6 weeks · CNY 120,000
Payment
Paid on acceptance of each phase
DiscountPlease check
5% off, beyond your approval limit

Send this quote to the customer?

AI only suggests; you decide.

Contract

Before Legal compared the contract with the quote clause by clause, over several rounds.

  • Contract amount, phases and payment terms match the quote.
  • The penalty in clause 8 is higher than the standard template; it is marked.

Send this version for signing?

AI only suggests; you decide.

Renewal reminder

Before Remembering only when the contract was nearly up, by which time the customer was looking elsewhere.

  • Sample customer A’s contract ends in 60 days; usage has been active for 3 months.
  • Suggestion: book a check-in next week and bring the new features.

6 steps in all; you confirm only 2.

ERP One purchase: from low stock to payment

Low-stock alert

Before Shortages surfaced at the month-end count, then everything was rushed.

  • Material M-102 is below its safety level and will run out in about 3 days.
  • Based on the last 4 weeks of use, suggest reordering 2,000 units.

Purchase order

Before Buyers dug through past orders, compared prices and filled in the form by hand.

Material
M-102
QuantityPlease check
2,000 units
Supplier
Sample Materials Co. (most reliable delivery over the last 6 orders)
Amount
CNY 128,000

Submit this purchase order?

AI only suggests; you decide.

Contract extraction

Before Copying each field from the PDF into the system by hand.

Sample purchase contract PO-0815.pdf

Supplier
Sample Materials Co.
Amount
CNY 128,000
Delivery date
2026-11-20
Payment termsPlease check
Within 30 days of delivery

Delivery check

Before Finance matched the order, the goods receipt and the invoice line by line.

  • Order, goods receipt and invoice agree: 2,000 units, CNY 128,000.
  • The invoice tax rate matches the contract.

Payment

Before Every payment was checked by hand, then queued for approval.

Payment request · Sample Materials Co.

  • All three documents agree; the goods arrived 28 days ago, within the 30-day terms.
  • 2 other payments fall due this week; funds are sufficient.

Pay this on Friday?

AI only suggests; you decide.

Weekly operating report

Before Half of every Monday spent stitching numbers together from different sheets.

Operating report · Week 43 (illustration)

  • Purchasing: 12 orders placed this week, 2 awaiting approval.
  • Inventory: 1 material below safety stock, restock reminder sent.
  • Receivables: 3 payments received, 1 overdue and being followed up.

6 steps in all; you confirm only 2.

OA One expense claim: from submission to the books

Receipt reading

Before Staff typed in every receipt and finance checked them again.

Shanghai trip · 7 receipts uploaded (illustration)

Flights
2 receipts · CNY 1,860
Hotel
3 receipts · 3 nights · CNY 2,400
Taxis
2 receipts · CNY 146

Policy check

Before Searching the policy file for the limit, and everyone read it differently.

Q: What is the hotel limit on business trips?

  • No more than CNY 600 a night in tier-1 cities, CNY 450 elsewhere.

Source: travel policy 3.2 (illustration)

Approval

Before Approvers looked through every receipt and often missed what was over the limit.

Expense claim · Shanghai trip, 3 nights · Hotel CNY 2,400

  • Hotel is CNY 800 a night, above the tier-1 city limit (per travel policy 3.2).
  • Everything else is within policy.

Approve this expense claim?

AI only suggests; you decide.

Booking

Before Accountants made each voucher by hand from the claim.

Debit: travel expenses
CNY 3,806
Credit: other payables · staff member D
CNY 3,806
NotePlease check
The CNY 600 over the hotel limit is borne by the employee

Create this accounting voucher?

AI only suggests; you decide.

Filing

Before Paper receipts were bound, numbered and stored somewhere.

  • All 7 original receipts are filed and linked to the voucher.
  • They can be found by employee, project or month.

Spending review

Before Exporting the month’s detail and building pivot tables in Excel.

  • 4 travel claims went over the limit this month, 3 of them for hotels in tier-1 cities.
  • Suggestion: spell out the tier-1 hotel limit in the policy to cut down on returned claims.

6 steps in all; you confirm only 2.

FMCG Mid-Autumn promotion: from order to expense claim

Order

Before Sales reps phoned each distributor about stock, then guessed the reorder.

Sample distributor H · Mid-Autumn stocking suggestion

Lead product
1,200 cases (from the last 4 weeks of sales)
Gift boxesPlease check
300 cases
Arrival
By 5 September

Place this order with the distributor?

AI only suggests; you decide.

Shipping

Before The office booked trucks, printed slips and called the warehouse, a dozen calls a day.

  • 3 delivery notes created and combined into 2 trucks.
  • Expected to arrive on 3 September.

Shelf check

Before Supervisors visited store after store and went through the photos.

Sample store K · 12 store photos

  • The main shelf uses fewer than the agreed 3 rows.
  • 2 price tags are missing, and 1 does not match the promotion price.
  • The fridge display meets the standard.

Sell-through

Before Waiting for distributors’ month-end figures, so problems showed up too late.

  • 15 of 18 stores are selling normally.
  • Sample stores K, L and M are slow on gift boxes: suggest one more tasting.

Expense claim

Before Finance checked photos against agreements, one store at a time.

Sample distributor H · Mid-Autumn display claim

Campaign
Mid-Autumn floor display
Stores
18
Amount claimed
CNY 36,000
Display photosPlease check
Uploaded for 16 stores

Photos are missing for 2 stores. Approve the claim for 16 for now?

AI only suggests; you decide.

Review

Before The wrap-up came a month after the campaign ended.

  • Gift boxes sold best in neighbourhood stores and slower in supermarkets.
  • Next year: more gift boxes to neighbourhood stores, fewer to supermarkets.

6 steps in all; you confirm only 2.

New media One launch post: from topic to review

Topics

Before Editors scrolled trending lists and met to pick topics.

  • Trending this week: autumn launches and back to school.
  • Suggested topic: “The new recipe in three minutes”, suited to short video.
  • Suggested topic: “An office afternoon-tea list”, suited to an image post.

Draft

Before Pulling selling points out of product documents; one draft took a whole day.

First draft: autumn product launch (illustration)

  • Title: The new recipe in three minutes.
  • 6 sections: recipe, taste, occasions, price, where to buy, offers.
  • Every product detail comes from the product documents, with sources marked.

Channel versions

Before Rewriting the same post by hand for every platform.

Original: autumn product launch (illustration)

Xiaohongshu
Title with emoji, body in 5 parts, 6 images
Douyin
A 45-second spoken script, selling point in the first 3 seconds
WeChat article
Full text kept, plus a short product story

Publish

Before Logging in to each platform, uploading and scheduling one by one.

Xiaohongshu
Thursday 12:00
Douyin
Thursday 18:30
WeChat article
Friday 8:00

Publish on this schedule?

AI only suggests; you decide.

Comment replies

Before Reading and answering comments one at a time.

214 comments under the launch video (illustration)

  • 86 ask about price and where to buy: suggest one reply pointing to the pinned link.
  • 23 say it is too sweet: suggest an honest reply that mentions the low-sugar version.
  • 2 look like spam: suggest hiding them.

Reply to these comments as suggested?

AI only suggests; you decide.

Results review

Before Exporting data from every platform and stitching it into one sheet.

  • Douyin had the best watch-through; the version with the selling point in the first 3 seconds did better.
  • Xiaohongshu got saves but few comments: add a question for readers next time.

6 steps in all; you confirm only 2.

E-commerce One new product: from listing to after-sales

Listing copy

Before Writing titles, selling points and detail pages by hand from the specs.

Sample product · 500 ml vacuum flask

Title
316 stainless steel 500 ml flask, long-lasting heat
Selling points
Hot for 12 hours · one-touch lid · fits cup holders
Detail page
6 sections drafted: material, capacity, cleaning and more
SizePlease check
22 cm tall, 7 cm across

List the product with this copy?

AI only suggests; you decide.

Pricing and stock

Before Watching competitor prices, working out margins and guessing stock, all in spreadsheets.

  • Similar products sell for CNY 79–109; suggest pricing at CNY 89.
  • Based on pre-orders, stock 3,000 units in the first batch.

Customer questions

Before Agents answered the same questions over and over.

312 questions today (illustration)

  • 268 answered by AI from the product details: capacity, material, dishwasher-safe or not.
  • 44 passed to a person: after-sales, address changes, invoices.

Why the bad reviews

Before Reading hundreds of reviews and guessing at the causes.

37 bad reviews in the last 30 days (illustration)

  • 15 are about damage in transit, mostly with one sample courier: suggest thicker packaging.
  • 12 are about a leaking lid, all from one batch: suggest asking the factory to check.
  • 10 say it differs from the listing, a colour mismatch: suggest new real-life photos.

Refund check

Before Refunds were either all checked by hand or all approved automatically.

Refund request · order A20931 (illustration)

  • This buyer has asked for 6 refunds in 90 days, 4 of them refund-only.
  • Tracking shows the order was signed for; the buyer says it never arrived.

Send to a person for review instead of refunding automatically?

AI only suggests; you decide.

Weekly report

Before Pulling reports, taking screenshots and writing a summary every week.

  • 1,240 units sold this week; the refund rate is normal for the category.
  • The batch with leaking lids is off sale, and exchanges are under way.

6 steps in all; you confirm only 2.

Chains One store’s week

Rota

Before The manager built the rota by hand from footfall and leave requests.

Sample store L · next week’s rota

Friday evening rush
Add 1 person
Tuesday afternoon
Quiet; 1 fewer person is enough
Staff member CPlease check
46 hours scheduled this week, near the limit

Publish this rota?

AI only suggests; you decide.

Opening checks

Before A paper checklist, filled in or not on trust.

  • 11 of 12 opening checks done.
  • No photo of the fridge temperature: the staff member on shift has been reminded.

Inspections

Before The area manager drove from store to store.

This week’s inspections · 24 stores (illustration)

  • 21 stores passed.
  • Sample store L: the kitchen temperature log is missing 2 days.
  • Sample stores M and N: opening checklists were not submitted on time.

Reordering

Before The manager guessed order sizes from experience, often over-ordering or running out.

Milk
40 cases (busy weekend ahead)
Bread
120 loaves
Coffee beans
None needed this week

Review replies

Before The manager answered reviews one by one after closing.

Bad review: waited 40 minutes and nobody came over (illustration)

  • Draft: We’re very sorry you had to wait. We’ve added hosts for weekend evening rushes; show this review on your next visit and dessert is on us.

Use this reply?

AI only suggests; you decide.

Weekly report

Before The manager wrote a weekly report by hand for the area manager.

  • More customers than last week; the Friday evening rush had the longest waits.
  • Both bad reviews were about waiting; more staff have been added.

6 steps in all; you confirm only 2.

Education One student: from enquiry to renewal

Enquiry follow-up

Before Advisers scrolled through WeChat and followed up with whoever came to mind.

  • Sample parent P asked about kids’ coding and has not replied for 3 days. Suggestion: send a trial-class introduction today.
  • Sample parent Q said “we’ll think about it” after a trial class. Suggestion: send the timetable and teacher profiles at the weekend.

Trial class

Before Going back and forth over teachers’ timetables to find a slot.

Student
Sample student R · age 9
Course
Primary maths trial
Time
Saturday 10:00 · teacher T

Enrolment

Before After sign-up: creating the record, placing the class and sending notices by hand.

  • Placed in the Saturday morning class by age and level.
  • The welcome notice and preparation list are drafted and ready to send.

Homework marking

Before Teachers marked homework late into the night.

Sample student R · maths homework, lesson 12

Completed
9 of 10 questions
Main issue
3 mistakes with common denominators
Practice
5 common-denominator questions
Question 7Please check
Different method; AI marked it wrong, teacher to confirm

Parent feedback

Before Writing feedback to every parent by hand each week.

This week’s note to sample student R’s parents

  • Draft: R handed in every piece of homework on time this week. Common denominators still need work, so we’ve set 5 practice questions and will go over it again in class next week.

Send this note?

AI only suggests; you decide.

Renewal reminder

Before Remembering only when lessons ran out, by which time parents were comparing other schools.

Sample student R · 4 lessons left

  • Draft: R has made clear progress this term and handed in homework on time. Next term’s timetable is out, and renewing now keeps the same time and teacher.

Send this renewal reminder?

AI only suggests; you decide.

6 steps in all; you confirm only 2.

Manufacturing One order: from intake to delivery

Order review

Before Sales, production and purchasing met to check capacity and materials.

  • Sample order S-1107: 5,000 parts, due 20 November.
  • Capacity and materials are both enough to take it on.

Scheduling

Before Planners shuffled orders around on whiteboards and spreadsheets.

Sample order S-1107 needed 3 days early

Change
Move S-1107 to Wednesday
EffectPlease check
S-1102 slips 1 day, still on time
Materials
Enough; no top-up needed

Adjust the schedule this way?

AI only suggests; you decide.

Materials picking

Before The warehouse picked from paper slips and often got it wrong.

  • 3 picking lists created from the schedule.
  • All materials are in; production starts on time on Tuesday.

QC root causes

Before Inspectors kept handwritten records and guessed causes from experience.

42 parts failed QC this week (illustration)

  • 31 are out of tolerance, mostly from machine 2 on the night shift.
  • 8 have surface scratches, linked to the new transfer crates.
  • 3 have no clear cause: suggest spot checks.

Equipment alert

Before Machines were fixed only after they broke, and stopped lines were costly.

  • Injection moulder 2: vibration has risen for 3 days and is near the alarm line. Suggestion: stop it for a check this week.
  • Air compressor B: using more power than the same time last month. Suggestion: check for leaks.

Release for delivery

Before Before shipping, someone went through QC records and counted by hand.

  • All 5,000 parts inspected; rejects are set aside for rework.
  • The delivery note and QC report are ready.

Release this batch to ship?

AI only suggests; you decide.

6 steps in all; you confirm only 2.

A scripted illustration, not real customer data.

Security

Security and reliability: a human in the loop

Let AI do what it is good at, and leave the key decisions to people.

  1. 01AI suggests
  2. 02A person confirms
  3. 03The system acts
  4. 04Everything is logged
Will AI go off and do things on its own?
Key actions such as writing data, approvals and sending anything outside need a person to confirm before they run; when AI is unsure, it hands over to a person.
Is our data safe?
Permissions follow roles, and AI reads only the data a task needs; content sent to a model is de-identified first; on-premises or private deployment is available on request.
Can we trace what went wrong?
Who did what and when, what AI suggested and on what basis, are all recorded and can be rolled back.
What if AI gets something wrong?
Answers cite their sources so they are easy to check; questions beyond its ability get a clear “I don’t know”.

Research shows that on tasks beyond AI’s ability, consultants using AI were 19% less likely to reach a correct answer (Dell'Acqua et al., Organization Science, 2026). That is why key steps need a person in charge.

How we keep it safe

Evidence

Efficiency and quality, on the record

AI can make work faster and better, and there are things it does badly. These figures come from published research, with the source and who was studied.

Estimate what your team could save

40%

Default 40%, the top of the published research (about 15%–40%); repetitive work such as data entry, checking and tidying usually goes higher, so adjust as you see fit.

Hours freed per year

2,880 hours

Equivalent labour cost

$86,400

This estimate is based on results for specific tasks in published research, over 48 working weeks a year. Real results depend on your processes, data quality and how practised people are, and quality can drop on tasks beyond what AI does well. It is not a promise of returns.

Process

Working together

From asking to finding the path, in four steps.

  1. 01

    Question

    We frame the problem with you: the goal, the constraints, and what success means.

    What you get
    A written statement of the problem and what success means
    What we need from you
    A walkthrough of your process and one person who can decide
  2. 02

    Path

    We propose the approach: how, how far, and what each stage delivers.

    What you get
    A plan with phases and a schedule
    What we need from you
    Your sign-off on the plan
  3. 03

    Build

    We build in small steps, so every stage can be seen, tried and commented on.

    What you get
    A working version you can try at every phase
    What we need from you
    Feedback from trying it, plus representative sample data
  4. 04

    Deliver

    Launch, documentation and handover, so your team can carry it on.

    What you get
    Go-live, documentation and handover
    What we need from you
    A colleague to take it over

FAQ

A few common questions

How long does it take?

It depends on scope. A single agent or a simple demo is usually quicker; connecting several systems takes longer. After we talk, you get a stage-by-stage schedule rather than one vague number.

How are data security and privacy handled?

Before we start we agree with you which data may be used, where it is processed and who can access it. The specific confidentiality terms and deployment approach are set out in the agreement.

Which model do you use, and what does it cost?

We pick a model per task, balancing quality, speed and cost, and taking into account where your data may be kept. Model usage fees are described in the proposal so you can estimate them.

See all questions →

Projects

Project examples

A few kinds of systems we have built, with client and data details removed. They all tackle the same kind of problem: work that is repetitive, scattered and easy to get wrong.

  • Financial services · SaaS platform

    Credit diagnosis and service platform

    Before
    Credit diagnoses drew on files scattered everywhere, and consultants wrote conclusions by hand. Months later, nobody could say why a call had been made.
    Now
    Profile, diagnosis, repair plan and follow-up run in one system. AI reads the files and drafts the report, and every conclusion links back to its source. Nothing takes effect until a consultant confirms, and each client’s data is kept apart.
    • AI-drafted reports
    • Traceable conclusions
    • Consultant sign-off
    • Each client’s data kept apart
  • Property management · mini programs and admin

    Community property service platform

    Before
    Repairs, payments, visitors and complaints all came in by phone and chat group. Messages got lost, residents chased for updates, and the office could not say where things stood.
    Now
    Residents report repairs, check bills and register visitors in a mini program and can see progress at any time. Staff assign work, reply, post notices and run votes from one console. Every request is on record from start to finish.
    • Resident mini program
    • Staff workspace
    • Web admin console
  • Insurance · finance tooling

    Premium reconciliation tool

    Before
    Every partner’s premium file had its own layout, so finance matched them line by line by hand. It took ages, and things slipped through.
    Now
    Drop both files in and a list of differences comes straight out, with commissions filled in automatically. Each company’s layout is set up once and reused. Data stays on the computer; double-click to run.
    • Reads every layout
    • Line-by-line matching
    • Difference list
  • Team workflow · agent

    Project platform with a Feishu bot

    Before
    Project talk lived in group chats and sank out of sight. Milestones, tasks, bugs and customer feedback were scattered, and finding out who owned what meant scrolling back through messages.
    Now
    Mention the bot in a group and it turns the discussion into tasks, answers progress questions and proposes changes; nothing is stored until you confirm. It reminds people before things go overdue. Customer feedback keeps its original wording and becomes a bug or requirement in one click.
    • Works inside Feishu groups
    • Stored only after confirmation
    • Overdue reminders

Curious which work AI could take on?

Tell us where your team spends the most time and effort, and we’ll work out together whether it’s worth doing and how.

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